Neural net-based use of perceptrons to mimic human senses associated with a vehicle occupant

CROSS-REFERENCE TO RELATED APPLICATIONS

0001This application is a continuation of U.S. application Ser. No. 17/977,550, filed Oct. 31, 2022, which is continuation of U.S. application Ser. No. 16/887,557, filed May 29, 2020, which is a continuation of U.S. application Ser. No. 16/803,220, filed Feb. 27, 2020, which is a continuation of International Application Ser. No. PCT/US2019/053857, filed Sep. 30, 2019, which itself claims priority to U.S. provisional application No. 62/739,335, filed Sep. 30, 2018, which are hereby incorporated by reference as if fully set forth herein in their entireties.

TECHNICAL FIELD

0002The present disclosure relates to intelligent transportation systems, and in examples, more particularly relates to inter-connectivity and optimization of user experiences in transportation systems.

BACKGROUND

0003As artificial intelligence, cognitive networking, sensor technologies, storage technologies (e.g., blockchain and other distributed ledger technologies) and other technologies progress, opportunities exist for development of systems that enable improved mobility and transportation for passengers and for objects, such as freight, goods, animals and the like. A need exists for improved transportation systems that take advantage of such technologies and their capabilities.

0004Some applications of artificial intelligence have been, at least to a degree, effective at accomplishing certain tasks, such as tasks involving recognition and classification of objects and behavior, such as in natural language processing (NLP) and computer vision systems. However, in complex, dynamic systems that involve interactions of elements, such as transportation systems that involve sets of complex chemical processes (e.g., involving combustion processes, heating and cooling, battery charging and discharging), mechanical systems, and human systems (involving individual and group behaviors), significant challenges exist in classifying, predicting and optimizing system-level interactions and behaviors. A need exists for systems apply specialized capabilities of different types of neural networks and other artificial intelligence technologies and for systems that enable selective deployment of such technologies, as well as various hybrids and combinations of such technologies.

SUMMARY

0005Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system,” which terms should be understood to encompass any of the above except where context indicates otherwise) that individually or collectively enable advances in transportation systems.

0006An aspect provided herein includes a method of optimizing an operating state of a vehicle, the method comprising: classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system; predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system; and optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

0007In embodiments, at least one of the neural networks in the hybrid neural network is a convolutional neural network. In embodiments, the social media data includes social media posts. In embodiments, the social media data includes social media feeds. In embodiments, the social media data includes like or dislike activity detected in the social media. In embodiments, the social media data includes indications of relationships. In embodiments, the social media data includes user behavior. In embodiments, the social media data includes discussion threads. In embodiments, the social media data includes chats. In embodiments, the social media data includes photographs.

0008In embodiments, the social media data includes traffic-affecting information. In embodiments, the social media data includes an indication of a specific individual at a location. In embodiments, the social media data includes an indication of a celebrity at a location. In embodiments, the social media data includes presence of a rare or transient phenomena at a location. In embodiments, the social media data includes a commerce-related event. In embodiments, the social media data includes an entertainment event at a location. In embodiments, the social media data includes traffic conditions. In embodiments, the social media data includes weather conditions. In embodiments, the social media data includes entertainment options.

0009In embodiments, the social media data includes risk-related conditions. In embodiments, the social media data includes predictions of attendance at an event. In embodiments, the social media data includes estimates of attendance at an event. In embodiments, the social media data includes modes of transportation used with an event. In embodiments, the effect on the transportation system includes reducing fuel consumption. In embodiments, the effect on the transportation system includes reducing traffic congestion. In embodiments, the effect on the transportation system includes reduced carbon footprint. In embodiments, the effect on the transportation system includes reduced pollution.

0010In embodiments, the optimized state of the at least one vehicle is an operating state of the vehicle. In embodiments, the optimized state of the at least one vehicle includes an in-vehicle state. In embodiments, the optimized state of the at least one vehicle includes a rider state. In embodiments, the optimized state of the at least one vehicle includes a routing state. In embodiments, the optimized state of the at least one vehicle includes user experience state. In embodiments, a characterization of an outcome of the optimizing in the social media data is used as feedback to improve the optimizing. In embodiments, the feedback includes likes and dislikes of the outcome. In embodiments, the feedback includes social medial activity referencing the outcome.

0011In embodiments, the feedback includes trending of social media activity referencing the outcome. In embodiments, the feedback includes hash tags associated with the outcome. In embodiments, the feedback includes ratings of the outcome. In embodiments, the feedback includes requests for the outcome.

0012It is to be understood that any combination of features from the methods disclosed herein and/or from the systems disclosed herein may be used together, and/or that any features from any or all of these aspects may be combined with any of the features of the embodiments and/or examples disclosed herein to achieve the benefits as described in this disclosure.

BRIEF DESCRIPTION OF THE FIGURES

0013In the accompanying figures, like reference numerals refer to identical or functionally similar elements throughout the separate views and together with the detailed description below are incorporated in and form part of the specification, serve to further illustrate various embodiments and to explain various principles and advantages all in accordance with the systems and methods disclosed herein.

0014<figref idref="DRAWINGS">FIG. <b>1</b></figref> is a diagrammatic view that illustrates an architecture for a transportation system showing certain illustrative components and arrangements relating to various embodiments of the present disclosure.

0015<figref idref="DRAWINGS">FIG. <b>2</b></figref> is a diagrammatic view that illustrates use of a hybrid neural network to optimize a powertrain component of a vehicle relating to various embodiments of the present disclosure.

0016<figref idref="DRAWINGS">FIG. <b>3</b></figref> is a diagrammatic view that illustrates a set of states that may be provided as inputs to and/or be governed by an expert system/Artificial Intelligence (AI) system relating to various embodiments of the present disclosure.

0017<figref idref="DRAWINGS">FIG. <b>4</b></figref> is a diagrammatic view that illustrates a range of parameters that may be taken as inputs by an expert system or AI system, or component thereof, as described throughout this disclosure, or that may be provided as outputs from such a system and/or one or more sensors, cameras, or external systems relating to various embodiments of the present disclosure.

0018<figref idref="DRAWINGS">FIG. <b>5</b></figref> is a diagrammatic view that illustrates a set of vehicle user interfaces relating to various embodiments of the present disclosure.

0019<figref idref="DRAWINGS">FIG. <b>6</b></figref> is a diagrammatic view that illustrates a set of interfaces among transportation system components relating to various embodiments of the present disclosure.

0020<figref idref="DRAWINGS">FIG. <b>7</b></figref> is a diagrammatic view that illustrates a data processing system, which may process data from various sources relating to various embodiments of the present disclosure.

0021<figref idref="DRAWINGS">FIG. <b>8</b></figref> is a diagrammatic view that illustrates a set of algorithms that may be executed in connection with one or more of the many embodiments of transportation systems described throughout this disclosure relating to various embodiments of the present disclosure.

0022<figref idref="DRAWINGS">FIG. <b>9</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0023<figref idref="DRAWINGS">FIG. <b>10</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0024<figref idref="DRAWINGS">FIG. <b>11</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0025<figref idref="DRAWINGS">FIG. <b>12</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0026<figref idref="DRAWINGS">FIG. <b>13</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0027<figref idref="DRAWINGS">FIG. <b>14</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0028<figref idref="DRAWINGS">FIG. <b>15</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0029<figref idref="DRAWINGS">FIG. <b>16</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0030<figref idref="DRAWINGS">FIG. <b>17</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0031<figref idref="DRAWINGS">FIG. <b>18</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0032<figref idref="DRAWINGS">FIG. <b>19</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0033<figref idref="DRAWINGS">FIG. <b>20</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0034<figref idref="DRAWINGS">FIG. <b>21</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0035<figref idref="DRAWINGS">FIG. <b>22</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0036<figref idref="DRAWINGS">FIG. <b>23</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0037<figref idref="DRAWINGS">FIG. <b>24</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0038<figref idref="DRAWINGS">FIG. <b>25</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0039<figref idref="DRAWINGS">FIG. <b>26</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0040<figref idref="DRAWINGS">FIG. <b>26</b>A</figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0041<figref idref="DRAWINGS">FIG. <b>27</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0042<figref idref="DRAWINGS">FIG. <b>28</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0043<figref idref="DRAWINGS">FIG. <b>29</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0044<figref idref="DRAWINGS">FIG. <b>30</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0045<figref idref="DRAWINGS">FIG. <b>31</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0046<figref idref="DRAWINGS">FIG. <b>32</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0047<figref idref="DRAWINGS">FIG. <b>33</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0048<figref idref="DRAWINGS">FIG. <b>34</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0049<figref idref="DRAWINGS">FIG. <b>35</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0050<figref idref="DRAWINGS">FIG. <b>36</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0051<figref idref="DRAWINGS">FIG. <b>37</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0052<figref idref="DRAWINGS">FIG. <b>38</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0053<figref idref="DRAWINGS">FIG. <b>39</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0054<figref idref="DRAWINGS">FIG. <b>40</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0055<figref idref="DRAWINGS">FIG. <b>41</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0056<figref idref="DRAWINGS">FIG. <b>42</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0057<figref idref="DRAWINGS">FIG. <b>43</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0058<figref idref="DRAWINGS">FIG. <b>44</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0059<figref idref="DRAWINGS">FIG. <b>45</b></figref> is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.

0060<figref idref="DRAWINGS">FIG. <b>46</b></figref> is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.

0061<figref idref="DRAWINGS">FIG. <b>47</b></figref> is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.

0062<figref idref="DRAWINGS">FIG. <b>48</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0063<figref idref="DRAWINGS">FIG. <b>49</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0064<figref idref="DRAWINGS">FIG. <b>50</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0065<figref idref="DRAWINGS">FIG. <b>51</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0066<figref idref="DRAWINGS">FIG. <b>52</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0067<figref idref="DRAWINGS">FIG. <b>53</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0068<figref idref="DRAWINGS">FIG. <b>54</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0069<figref idref="DRAWINGS">FIG. <b>55</b></figref> is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

0070<figref idref="DRAWINGS">FIG. <b>56</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0071<figref idref="DRAWINGS">FIG. <b>57</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0072<figref idref="DRAWINGS">FIG. <b>58</b></figref> is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

0073Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of the many embodiments of the systems and methods disclosed herein.

DETAILED DESCRIPTION

0074The present disclosure will now be described in detail by describing various illustrative, non-limiting embodiments thereof with reference to the accompanying drawings and exhibits. The disclosure may, however, be embodied in many different forms and should not be construed as being limited to the illustrative embodiments set forth herein. Rather, the embodiments are provided so that this disclosure will be thorough and will fully convey the concept of the disclosure to those skilled in the art. The claims should be consulted to ascertain the true scope of the disclosure.

0075Before describing in detail embodiments that are in accordance with the systems and methods disclosed herein, it should be observed that the embodiments reside primarily in combinations of method and/or system components. Accordingly, the system components and methods have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the systems and methods disclosed herein.

0076All documents mentioned herein are hereby incorporated by reference in their entirety. References to items in the singular should be understood to include items in the plural, and vice versa, unless explicitly stated otherwise or clear from the context. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations of conjoined clauses, sentences, words, and the like, unless otherwise stated or clear from the context. Thus, the term “or” should generally be understood to mean “and/or” and so forth, except where the context clearly indicates otherwise.

0077Recitation of ranges of values herein are not intended to be limiting, referring instead individually to any and all values falling within the range, unless otherwise indicated herein, and each separate value within such a range is incorporated into the specification as if it were individually recited herein. The words “about,” “approximately,” or the like, when accompanying a numerical value, are to be construed as indicating a deviation as would be appreciated by one skilled in the art to operate satisfactorily for an intended purpose. Ranges of values and/or numeric values are provided herein as examples only, and do not constitute a limitation on the scope of the described embodiments. The use of any and all examples, or exemplary language (“e.g.,” “such as,” or the like) provided herein, is intended merely to better illuminate the embodiments and does not pose a limitation on the scope of the embodiments or the claims. No language in the specification should be construed as indicating any unclaimed element as essential to the practice of the embodiments.

0078In the following description, it is understood that terms such as “first,” “second,” “third,” “above,” “below,” and the like, are words of convenience and are not to be construed as implying a chronological order or otherwise limiting any corresponding element unless expressly stated otherwise. The term “set” should be understood to encompass a set with a single member or a plurality of members.

0079Referring to <figref idref="DRAWINGS">FIG. <b>1</b></figref>, an architecture for a transportation system <b>111</b> is depicted, showing certain illustrative components and arrangements relating to certain embodiments described herein. The transportation system <b>111</b> may include one or more vehicles <b>110</b>, which may include various mechanical, electrical, and software components and systems, such as a powertrain <b>113</b>, a suspension system <b>117</b>, a steering system, a braking system, a fuel system, a charging system, seats <b>128</b>, a combustion engine, an electric vehicle drive train, a transmission <b>119</b>, a gear set, and the like. The vehicle may have a vehicle user interface <b>123</b>, which may include a set of interfaces that include a steering system, buttons, levers, touch screen interfaces, audio interfaces, and the like as described throughout this disclosure. The vehicle may have a set of sensors <b>125</b> (including cameras <b>127</b>), such as for providing input to expert system/artificial intelligence features described throughout this disclosure, such as one or more neural networks (which may include hybrid neural networks <b>147</b> as described herein). Sensors <b>125</b> and/or external information may be used to inform the expert system/Artificial Intelligence (AI) system <b>136</b> and to indicate or track one or more vehicle states <b>144</b>, such as vehicle operating states <b>345</b> (<figref idref="DRAWINGS">FIG. <b>3</b></figref>), user experience states <b>346</b> (<figref idref="DRAWINGS">FIG. <b>3</b></figref>), and others described herein, which also may be as inputs to or taken as outputs from a set of expert system/AI components. Routing information <b>143</b> may inform and take input from the expert system/AI system <b>136</b>, including using in-vehicle navigation capabilities and external navigation capabilities, such as Global Position System (GPS), routing by triangulation (such as cell towers), peer-to-peer routing with other vehicles <b>121</b>, and the like. A collaboration engine <b>129</b> may facilitate collaboration among vehicles and/or among users of vehicles, such as for managing collective experiences, managing fleets and the like. Vehicles <b>110</b> may be networked among each other in a peer-to-peer manner, such as using cognitive radio, cellular, wireless or other networking features. An AI system <b>136</b> or other expert systems may take as input a wide range of vehicle parameters <b>130</b>, such as from on board diagnostic systems, telemetry systems, and other software systems, as well as from vehicle-located sensors <b>125</b> and from external systems. In embodiments, the system may manage a set of feedback/rewards <b>148</b>, incentives, or the like, such as to induce certain user behavior and/or to provide feedback to the AI system <b>136</b>, such as for learning on a set of outcomes to accomplish a given task or objective. The expert system or AI system <b>136</b> may inform, use, manage, or take output from a set of algorithms <b>149</b>, including a wide variety as described herein. In the example of the present disclosure depicted in <figref idref="DRAWINGS">FIG. <b>1</b></figref>, a data processing system <b>162</b>, is connected to the hybrid neural network <b>147</b>. The data processing system <b>162</b> may process data from various sources (see <figref idref="DRAWINGS">FIG. <b>7</b></figref>). In the example of the present disclosure depicted in <figref idref="DRAWINGS">FIG. <b>1</b></figref>, a system user interface <b>163</b>, is connected to the hybrid neural network <b>147</b>. See the disclosure, below, relating to <figref idref="DRAWINGS">FIG. <b>6</b></figref> for further disclosure relating to interfaces. <figref idref="DRAWINGS">FIG. <b>1</b></figref> shows that vehicle surroundings <b>164</b> may be part of the transportation system <b>111</b>. Vehicle surroundings may include roadways, weather conditions, lighting conditions, etc. <figref idref="DRAWINGS">FIG. <b>1</b></figref> shows that devices <b>165</b>, for example, mobile phones and computer systems, navigation systems, etc., may be connected to various elements of the transportation system <b>111</b>, and therefore may be part of the transportation system <b>111</b> of the present disclosure.

0080Referring to <figref idref="DRAWINGS">FIG. <b>2</b></figref>, provided herein are transportation systems having a hybrid neural network <b>247</b> for optimizing a powertrain <b>213</b> of a vehicle, wherein at least two parts of the hybrid neural network <b>247</b> optimize distinct parts of the powertrain <b>213</b>. An artificial intelligence system may control a powertrain component <b>215</b> based on an operational model (such as a physics model, an electrodynamic model, a hydrodynamic model, a chemical model, or the like for energy conversion, as well as a mechanical model for operation of various dynamically interacting system components). For example, the AI system may control a powertrain component <b>215</b> by manipulating a powertrain operating parameter <b>260</b> to achieve a powertrain state <b>261</b>. The AI system may be trained to operate a powertrain component <b>215</b>, such as by training on a data set of outcomes (e.g., fuel efficiency, safety, rider satisfaction, or the like) and/or by training on a data set of operator actions (e.g., driver actions sensed by a sensor set, camera or the like or by a vehicle information system). In embodiments, a hybrid approach may be used, where one neural network optimizes one part of a powertrain (e.g., for gear shifting operations), while another neural network optimizes another part (e.g., braking, clutch engagement, or energy discharge and recharging, among others). Any of the powertrain components described throughout this disclosure may be controlled by a set of control instructions that consist of output from at least one component of a hybrid neural network <b>247</b>.

0081<figref idref="DRAWINGS">FIG. <b>3</b></figref> illustrates a set of states that may be provided as inputs to and/or be governed by an expert system/AI system <b>336</b>, as well as used in connection with various systems and components in various embodiments described herein. States <b>344</b> may include vehicle operating states <b>345</b>, including vehicle configuration states, component states, diagnostic states, performance states, location states, maintenance states, and many others, as well as user experience states <b>346</b>, such as experience-specific states, emotional states <b>366</b> for users, satisfaction states <b>367</b>, location states, content/entertainment states and many others.

0082<figref idref="DRAWINGS">FIG. <b>4</b></figref> illustrates a range of parameters <b>430</b> that may be taken as inputs by an expert system or AI system <b>136</b> (<figref idref="DRAWINGS">FIG. <b>1</b></figref>), or component thereof, as described throughout this disclosure, or that may be provided as outputs from such a system and/or one or more sensors <b>125</b> (<figref idref="DRAWINGS">FIG. <b>1</b></figref>), cameras <b>127</b> (<figref idref="DRAWINGS">FIG. <b>1</b></figref>), or external systems. Parameters <b>430</b> may include one or more goals <b>431</b> or objectives (such as ones that are to be optimized by an expert system/AI system, such as by iteration and/or machine learning), such as a performance goal <b>433</b>, such as relating to fuel efficiency, trip time, satisfaction, financial efficiency, safety, or the like. Parameters <b>430</b> may include market feedback parameters <b>435</b>, such as relating to pricing, availability, location, or the like of goods, services, fuel, electricity, advertising, content, or the like. Parameters <b>430</b> may include rider state parameters <b>437</b>, such as parameters relating to comfort <b>439</b>, emotional state, satisfaction, goals, type of trip, fatigue and the like. Parameters <b>430</b> may include parameters of various transportation-relevant profiles, such as traffic profiles <b>440</b> (location, direction, density and patterns in time, among many others), road profiles <b>441</b> (elevation, curvature, direction, road surface conditions and many others), user profiles, and many others. Parameters <b>430</b> may include routing parameters <b>442</b>, such as current vehicle locations, destinations, waypoints, points of interest, type of trip, goal for trip, required arrival time, desired user experience, and many others. Parameters <b>430</b> may include satisfaction parameters <b>443</b>, such as for riders (including drivers), fleet managers, advertisers, merchants, owners, operators, insurers, regulators and others. Parameters <b>430</b> may include operating parameters <b>444</b>, including the wide variety described throughout this disclosure.

0083<figref idref="DRAWINGS">FIG. <b>5</b></figref> illustrates a set of vehicle user interfaces <b>523</b>. Vehicle user interfaces <b>523</b> may include electromechanical interfaces <b>568</b>, such as steering interfaces, braking interfaces, interfaces for seats, windows, moonroof, glove box and the like. Interfaces <b>523</b> may include various software interfaces (which may have touch screen, dials, knobs, buttons, icons or other features), such as a game interface <b>569</b>, a navigation interface <b>570</b>, an entertainment interface <b>571</b>, a vehicle settings interface <b>572</b>, a search interface <b>573</b>, an ecommerce interface <b>574</b>, and many others. Vehicle interfaces may be used to provide inputs to, and may be governed by, one or more AI systems/expert systems such as described in embodiments throughout this disclosure.

0084<figref idref="DRAWINGS">FIG. <b>6</b></figref> illustrates a set of interfaces among transportation system components, including interfaces within a host system (such as governing a vehicle or fleet of vehicles) and host interfaces <b>650</b> between a host system and one or more third parties and/or external systems. Interfaces include third party interfaces <b>655</b> and end user interfaces <b>651</b> for users of the host system, including the in-vehicle interfaces that may be used by riders as noted in connection with <figref idref="DRAWINGS">FIG. <b>5</b></figref>, as well as user interfaces for others, such as fleet managers, insurers, regulators, police, advertisers, merchants, content providers, and many others. Interfaces may include merchant interfaces <b>652</b>, such as by which merchants may provide advertisements, content relating to offerings, and one or more rewards, such as to induce routing or other behavior on the part of users. Interfaces may include machine interfaces <b>653</b>, such as application programming interfaces (API) <b>654</b>, networking interfaces, peer-to-peer interfaces, connectors, brokers, extract-transform-load (ETL) system, bridges, gateways, ports and the like. Interfaces may include one or more host interfaces by which a host may manage and/or configure one or more of the many embodiments described herein, such as configuring neural network components, setting weight for models, setting one or more goals or objectives, setting reward parameters <b>656</b>, and many others. Interfaces may include expert system/AI system configuration interfaces <b>657</b>, such as for selecting one or more models <b>658</b>, selecting and configuring data sets <b>659</b> (such as sensor data, external data and other inputs described herein), AI selection <b>660</b> and AI configuration <b>661</b> (such as selection of neural network category, parameter weighting and the like), feedback selection <b>662</b> for an expert system/AI system, such as for learning, and supervision configuration <b>663</b>, among many others.

0085<figref idref="DRAWINGS">FIG. <b>7</b></figref> illustrates a data processing system <b>758</b>, which may process data from various sources, including social media data sources <b>769</b>, weather data sources <b>770</b>, road profile sources <b>771</b>, traffic data sources <b>772</b>, media data sources <b>773</b>, sensors sets <b>774</b>, and many others. The data processing system may be configured to extract data, transform data to a suitable format (such as for use by an interface system, an AI system/expert system, or other systems), load it to an appropriate location, normalize data, cleanse data, deduplicate data, store data (such as to enable queries) and perform a wide range of processing tasks as described throughout this disclosure.

0086<figref idref="DRAWINGS">FIG. <b>8</b></figref> illustrates a set of algorithms <b>849</b> that may be executed in connection with one or more of the many embodiments of transportation systems described throughout this disclosure. Algorithms <b>849</b> may take input from, provide output to, and be managed by a set of AI systems/expert systems, such as of the many types described herein. Algorithms <b>849</b> may include algorithms for providing or managing user satisfaction <b>874</b>, one or more genetic algorithms <b>875</b>, such as for seeking favorable states, parameters, or combinations of states/parameters in connection with optimization of one or more of the systems described herein. Algorithms <b>849</b> may include vehicle routing algorithms <b>876</b>, including ones that are sensitive to various vehicle operating parameters, user experience parameters, or other states, parameters, profiles, or the like described herein, as well as to various goals or objectives. Algorithms <b>849</b> may include object detection algorithms <b>876</b>. Algorithms <b>849</b> may include energy calculation algorithms <b>877</b>, such as for calculating energy parameters, for optimizing fuel usage, electricity usage or the like, for optimizing refueling or recharging time, location, amount or the like. Algorithms may include prediction algorithms, such as for a traffic prediction algorithm <b>879</b>, a transportation prediction algorithm <b>880</b>, and algorithms for predicting other states or parameters of transportation systems as described throughout this disclosure.

0087In various embodiments, transportation systems <b>111</b> as described herein may include vehicles (including fleets and other sets of vehicles), as well as various infrastructure systems. Infrastructure systems may include Internet of Things systems (such as using cameras and other sensors, such as disposed on or in roadways, on or in traffic lights, utility poles, toll booths, signs and other roadside devices and systems, on or in buildings, and the like), refueling and recharging systems (such as at service stations, charging locations and the like, and including wireless recharging systems that use wireless power transfer), and many others.

0088Vehicle electrical, mechanical and/or powertrain components as described herein may include a wide range of systems, including transmission, gear system, clutch system, braking system, fuel system, lubrication system, steering system, suspension system, lighting system (including emergency lighting as well as interior and exterior lights), electrical system, and various subsystems and components thereof.

0089Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed/acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.

0090Rider and/or user experience states and parameters as described throughout this disclosure may include emotional states, comfort states, psychological states (e.g., anxiety, nervousness, relaxation or the like), awake/asleep states, and/or states related to satisfaction, alertness, health, wellness, one or more goals or objectives, and many others. User experience parameters as described herein may further include ones related to driving, braking, curve approach, seat positioning, window state, ventilation system, climate control, temperature, humidity, sound level, entertainment content type (e.g., news, music, sports, comedy, or the like), route selection (such as for POIs, scenic views, new sites and the like), and many others.

0091In embodiments, a route may be ascribed various parameters of value, such as parameters of value that may be optimized to improve user experience or other factors, such as under control of an AI system/expert system. Parameters of value of a route may include speed, duration, on time arrival, length (e.g., in miles), goals (e.g., to see a Point of Interest (POI), to complete a task (e.g., complete a shopping list, complete a delivery schedule, complete a meeting, or the like), refueling or recharging parameters, game-based goals, and others. As one of many examples, a route may be attributed value, such as in a model and/or as an input or feedback to an AI system or expert system that is configured to optimize a route, for task completion. A user may, for example, indicate a goal to meet up with at least one of a set of friends during a weekend, such as by interacting with a user interface or menu that allows setting of objectives. A route may be configured (including with inputs that provide awareness of friend locations, such as by interacting with systems that include location information for other vehicles and/or awareness of social relationships, such as through social data feeds) to increase the likelihood of meeting up, such as by intersecting with predicted locations of friends (which may be predicted by a neural network or other AI system/expert system as described throughout this disclosure) and by providing in-vehicle messages (or messages to a mobile device) that indicates possible opportunities for meeting up.

0092Market feedback factors may be used to optimize various elements of transportation systems as described throughout this disclosure, such as current and predicted pricing and/or cost (e.g., of fuel, electricity and the like, as well as of goods, services, content and the like that may be available along the route and/or in a vehicle), current and predicted capacity, supply and/or demand for one or more transportation related factors (such as fuel, electricity, charging capacity, maintenance, service, replacement parts, new or used vehicles, capacity to provide ride sharing, self-driving vehicle capacity or availability, and the like), and many others.

0093An interface in or on a vehicle may include a negotiation system, such as a bidding system, a price-negotiating system, a reward-negotiating system, or the like. For example, a user may negotiate for a higher reward in exchange for agreeing to re-route to a merchant location, a user may name a price the user is willing to pay for fuel (which may be provided to nearby refueling stations that may offer to meet the price), or the like. Outputs from negotiation (such as agreed prices, trips and the like) may automatically result in reconfiguration of a route, such as one governed by an AI system/expert system.

0094Rewards, such as provided by a merchant or a host, among others, as described herein may include one or more coupons, such as redeemable at a location, provision of higher priority (such as in collective routing of multiple vehicles), permission to use a “Fast Lane,” priority for charging or refueling capacity, among many others. Actions that can lead to rewards in a vehicle may include playing a game, downloading an app, driving to a location, taking a photograph of a location or object, visiting a website, viewing or listening to an advertisement, watching a video, and many others.

0095In embodiments an AI system/expert system may use or optimize one or more parameters for a charging plan, such as for charging a battery of an electric or hybrid vehicle. Charging plan parameters may include routing (such as to charging locations), amount of charge or fuel provided, duration of time for charging, battery state, battery charging profile, time required to charge, value of charging, indicators of value, market price, bids for charging, available supply capacity (such as within a geofence or within a range of a set of vehicles), demand (such as based on detected charge/refueling state, based on requested demand, or the like), supply, and others. A neural network or other system (optionally a hybrid system as describe herein), using a model or algorithm (such as a genetic algorithm) may be used (such as by being trained over a set of trials on outcomes, and/or using a training set of human created or human supervised inputs, or the like) may provide a favorable and/or optimized charging plan for a vehicle or a set of vehicles based on the parameters. Other inputs may include priority for certain vehicles (e.g., for emergency responders or for those who have been rewarded priority in connection with various embodiments described herein).

0096In embodiments a processor, as described herein, may comprise a neural processing chip, such as one employing a fabric, such as a LambdaFabric. Such a chip may have a plurality of cores, such as 256 cores, where each core is configured in a neuron-like arrangement with other cores on the same chip. Each core may comprise a micro-scale digital signal processor, and the fabric may enable the cores to readily connect to the other cores on the chip. In embodiments, the fabric may connect a large number of cores (e.g., more than 500,000 cores) and/or chips, thereby facilitating use in computational environments that require, for example, large scale neural networks, massively parallel computing, and large-scale, complex conditional logic. In embodiments, a low-latency fabric is used, such as one that has latency of 400 nanoseconds, 300 nanoseconds, 200 nanoseconds, 100 nanoseconds, or less from device-to-device, rack-to-rack, or the like. The chip may be a low power chip, such as one that can be powered by energy harvesting from the environment, from an inspection signal, from an onboard antenna, or the like. In embodiments, the cores may be configured to enable application of a set of sparse matrix heterogeneous machine learning algorithms. The chip may run an object-oriented programming language, such as C++, Java, or the like. In embodiments, a chip may be programmed to run each core with a different algorithm, thereby enabling heterogeneity in algorithms, such as to enable one or more of the hybrid neural network embodiments described throughout this disclosure. A chip can thereby take multiple inputs (e.g., one per core) from multiple data sources, undertake massively parallel processing using a large set of distinct algorithms, and provide a plurality of outputs (such as one per core or per set of cores).

0097In embodiments a chip may contain or enable a security fabric, such as a fabric for performing content inspection, packet inspection (such as against a black list, white list, or the like), and the like, in addition to undertaking processing tasks, such as for a neural network, hybrid AI solution, or the like.

0098In embodiments, the platform described herein may include, integrate with, or connect with a system for robotic process automation (RPA), whereby an artificial intelligence/machine learning system may be trained on a training set of data that consists of tracking and recording sets of interactions of humans as the humans interact with a set of interfaces, such as graphical user interfaces (e.g., via interactions with mouse, trackpad, keyboard, touch screen, joystick, remote control devices); audio system interfaces (such as by microphones, smart speakers, voice response interfaces, intelligent agent interfaces (e.g., Siri and Alexa) and the like); human-machine interfaces (such as involving robotic systems, prosthetics, cybernetic systems, exoskeleton systems, wearables (including clothing, headgear, headphones, watches, wrist bands, glasses, arm bands, torso bands, belts, rings, necklaces and other accessories); physical or mechanical interfaces (e.g., buttons, dials, toggles, knobs, touch screens, levers, handles, steering systems, wheels, and many others); optical interfaces (including ones triggered by eye tracking, facial recognition, gesture recognition, emotion recognition, and the like); sensor-enabled interfaces (such as ones involving cameras, EEG or other electrical signal sensing (such as for brain-computer interfaces), magnetic sensing, accelerometers, galvanic skin response sensors, optical sensors, IR sensors, LIDAR and other sensor sets that are capable of recognizing thoughts, gestures (facial, hand, posture, or other), utterances, and the like, and others. In addition to tracking and recording human interactions, the RPA system may also track and record a set of states, actions, events and results that occur by, within, from or about the systems and processes with which the humans are engaging. For example, the RPA system may record mouse clicks on a frame of video that appears within a process by which a human review the video, such as where the human highlights points of interest within the video, tags objects in the video, captures parameters (such as sizes, dimensions, or the like), or otherwise operates on the video within a graphical user interface. The RPA system may also record system or process states and events, such as recording what elements were the subject of interaction, what the state of a system was before, during and after interaction, and what outputs were provided by the system or what results were achieved. Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision, such as by having a human correct the RPA system as it attempts in a set of trials to undertake the action that the human would have undertaken (e.g., tagging the right object, labeling an item correctly, selecting the correct button to trigger a next step in a process, or the like), such that over a set of trials the RPA system becomes increasingly effective at replicating the action the human would have taken. Learning may include deep learning, such as by reinforcing learning based on outcomes, such as successful outcomes (such as based on successful process completion, financial yield, and many other outcome measures described throughout this disclosure). In embodiments, an RPA system may be seeded during a learning phase with a set of expert human interactions, such that the RPA system begins to be able to replicate expert interaction with a system. For example, an expert driver's interactions with a robotic system, such as a remote-controlled vehicle or a UAV, may be recorded along with information about the vehicles state (e.g., the surrounding environment, navigation parameters, and purpose), such that the RPA system may learn to drive the vehicle in a way that reflects the same choices as an expert driver. After being taught to replicate the skills or expertise of an expert human, the RPA system may be transitioned to a deep learning mode, where the system further improves based on a set of outcomes, such as by being configured to attempt some level of variation in approach (e.g., trying different navigation paths to optimize time of arrival, or trying different approaches to deceleration and acceleration in curves) and tracking outcomes (with feedback), such that the RPA system can learn, by variation/experimentation (which may be randomized, rule-based, or the like, such as using genetic programming techniques, random-walk techniques, random forest techniques, and others) and selection, to exceed the expertise of the human expert. Thus, the RPA system learns from a human expert, acquires expertise in interacting with a system or process, facilitates automation of the process (such as by taking over some of the more repetitive tasks, including ones that require consistent execution of acquired skills), and provides a very effective seed for artificial intelligence, such as by providing a seed model or system that can be improved by machine learning with feedback on outcomes of a system or process.

0099RPA systems may have particular value in situations where human expertise or knowledge is acquired with training and experience, as well as in situations where the human brain and sensory systems are particularly adapted and evolved to solve problems that are computationally difficult or highly complex. Thus, in embodiments, RPA systems may be used to learn to undertake, among other things: visual pattern recognition tasks with respect to the various systems, processes, workflows and environments described herein (such as recognizing the meaning of dynamic interactions of objects or entities within a video stream (e.g., to understand what is taking place as humans and objects interact in a video); recognition of the significance of visual patterns (e.g., recognizing objects, structures, defects and conditions in a photograph or radiography image); tagging of relevant objects within a visual pattern (e.g., tagging or labeling objects by type, category, or specific identity (such as person recognition); indication of metrics in a visual pattern (such as dimensions of objects indicated by clicking on dimensions in an x-ray or the like); labeling activities in a visual pattern by category (e.g., what work process is being done); recognizing a pattern that is displayed as a signal (e.g., a wave or similar pattern in a frequency domain, time domain, or other signal processing representation); anticipate a n future state based on a current state (e.g., anticipating motion of a flying or rolling object, anticipating a next action by a human in a process, anticipating a next step by a machine, anticipating a reaction by a person to an event, and many others); recognize and predicting emotional states and reactions (such as based on facial expression, posture, body language or the like); apply a heuristic to achieve a favorable state without deterministic calculation (e.g., selecting a favorable strategy in sport or game, selecting a business strategy, selecting a negotiating strategy, setting a price for a product, developing a message to promote a product or idea, generating creative content, recognizing a favorable style or fashion, and many others); any many others. In embodiments, an RPA system may automate workflows that involve visual inspection of people, systems, and objects (including internal components), workflows that involve performing software tasks, such as involving sequential interactions with a series of screens in a software interface, workflows that involve remote control of robots and other systems and devices, workflows that involve content creation (such as selecting, editing and sequencing content), workflows that involve financial decision-making and negotiation (such as setting prices and other terms and conditions of financial and other transactions), workflows that involve decision-making (such as selecting an optimal configuration for a system or sub-system, selecting an optimal path or sequence of actions in a workflow, process or other activity that involves dynamic decision-making), and many others.

0100In embodiments, an RPA system may use a set of IoT devices and systems (such as cameras and sensors), to track and record human actions and interactions with respect to various interfaces and systems in an environment. The RPA system may also use data from onboard sensors, telemetry, and event recording systems, such as telemetry systems on vehicles and event logs on computers). The RPA system may thus generate and/or receive a large data set (optionally distributed) for an environment (such as any of the environments described throughout this disclosure) including data recording the various entities (human and non-human), systems, processes, applications (e.g., software applications used to enable workflows), states, events, and outcomes, which can be used to train the RPA system (or a set of RPA systems dedicated to automating various processes and workflows) to accomplish processes and workflows in a way that reflects and mimics accumulated human expertise, and that eventually improves on the results of that human expertise by further machine learning.

0101Referring to <figref idref="DRAWINGS">FIG. <b>9</b></figref>, in embodiments provided herein are transportation systems <b>911</b> having an artificial intelligence system <b>936</b> that uses at least one genetic algorithm <b>975</b> to explore a set of possible vehicle operating states <b>945</b> to determine at least one optimized operating state. In embodiments, the genetic algorithm <b>975</b> takes inputs relating to at least one vehicle performance parameter <b>982</b> and at least one rider state <b>937</b>.

0102An aspect provided herein includes a system for transportation <b>911</b>, comprising: a vehicle <b>910</b> having a vehicle operating state <b>945</b>; an artificial intelligence system <b>936</b> to execute a genetic algorithm <b>975</b> to generate mutations from an initial vehicle operating state to determine at least one optimized vehicle operating state. In embodiments, the vehicle operating state <b>945</b> includes a set of vehicle parameter values <b>984</b>. In embodiments, the genetic algorithm <b>975</b> is to: vary the set of vehicle parameter values <b>984</b> for a set of corresponding time periods such that the vehicle <b>910</b> operates according to the set of vehicle parameter values <b>984</b> during the corresponding time periods; evaluate the vehicle operating state <b>945</b> for each of the corresponding time periods according to a set of measures <b>983</b> to generate evaluations; and select, for future operation of the vehicle <b>910</b>, an optimized set of vehicle parameter values based on the evaluations.

0103In embodiments, the vehicle operating state <b>945</b> includes the rider state <b>937</b> of a rider of the vehicle. In embodiments, the at least one optimized vehicle operating state includes an optimized state of the rider. In embodiments, the genetic algorithm <b>975</b> is to optimize the state of the rider. In embodiments, the evaluating according to the set of measures <b>983</b> is to determine the state of the rider corresponding to the vehicle parameter values <b>984</b>.

0104In embodiments, the vehicle operating state <b>945</b> includes a state of the rider of the vehicle. In embodiments, the set of vehicle parameter values <b>984</b> includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm <b>975</b> is to optimize the state of the rider and the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures <b>983</b> is to determine the state of the rider and the state of performance of the vehicle corresponding to the vehicle performance control values.

0105In embodiments, the set of vehicle parameter values <b>984</b> includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm <b>975</b> is to optimize the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures <b>983</b> is to determine the state of performance of the vehicle corresponding to the vehicle performance control values.

0106In embodiments, the set of vehicle parameter values <b>984</b> includes a rider-occupied parameter value. In embodiments, the rider-occupied parameter value affirms a presence of a rider in the vehicle <b>910</b>. In embodiments, the vehicle operating state <b>945</b> includes the rider state <b>937</b> of a rider of the vehicle. In embodiments, the at least one optimized vehicle operating state includes an optimized state of the rider. In embodiments, the genetic algorithm <b>975</b> is to optimize the state of the rider. In embodiments, the evaluating according to the set of measures <b>983</b> is to determine the state of the rider corresponding to the vehicle parameter values <b>984</b>. In embodiments, the state of the rider includes a rider satisfaction parameter. In embodiments, the state of the rider includes an input representative of the rider. In embodiments, the input representative of the rider is selected from the group consisting of: a rider state parameter, a rider comfort parameter, a rider emotional state parameter, a rider satisfaction parameter, a rider goals parameter, a classification of trip, and combinations thereof.

0107In embodiments, the set of vehicle parameter values <b>984</b> includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm <b>975</b> is to optimize the state of the rider and the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures <b>983</b> is to determine the state of the rider and the state of performance of the vehicle corresponding to the vehicle performance control values. In embodiments, the set of vehicle parameter values <b>984</b> includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm <b>975</b> is to optimize the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures <b>983</b> is to determine the state of performance of the vehicle corresponding to the vehicle performance control values.

0108In embodiments, the set of vehicle performance control values are selected from the group consisting of: a fuel efficiency; a trip duration; a vehicle wear; a vehicle make; a vehicle model; a vehicle energy consumption profiles; a fuel capacity; a real-time fuel levels; a charge capacity; a recharging capability; a regenerative braking state; and combinations thereof. In embodiments, at least a portion of the set of vehicle performance control values is sourced from at least one of an on-board diagnostic system, a telemetry system, a software system, a vehicle-located sensor, and a system external to the vehicle <b>910</b>. In embodiments, the set of measures <b>983</b> relates to a set of vehicle operating criteria. In embodiments, the set of measures <b>983</b> relates to a set of rider satisfaction criteria. In embodiments, the set of measures <b>983</b> relates to a combination of vehicle operating criteria and rider satisfaction criteria. In embodiments, each evaluation uses feedback indicative of an effect on at least one of a state of performance of the vehicle and a state of the rider.

0109An aspect provided herein includes a system for transportation <b>911</b>, comprising: an artificial intelligence system <b>936</b> to process inputs representative of a state of a vehicle and inputs representative of a rider state <b>937</b> of a rider occupying the vehicle during the state of the vehicle with the genetic algorithm <b>975</b> to optimize a set of vehicle parameters that affects the state of the vehicle or the rider state <b>937</b>. In embodiments, the genetic algorithm <b>975</b> is to perform a series of evaluations using variations of the inputs. In embodiments, each evaluation in the series of evaluations uses feedback indicative of an effect on at least one of a vehicle operating state <b>945</b> and the rider state <b>937</b>. In embodiments, the inputs representative of the rider state <b>937</b> indicate that the rider is absent from the vehicle <b>910</b>. In embodiments, the state of the vehicle includes the vehicle operating state <b>945</b>. In embodiments, a vehicle parameter in the set of vehicle parameters includes a vehicle performance parameter <b>982</b>. In embodiments, the genetic algorithm <b>975</b> is to optimize the set of vehicle parameters for the state of the rider.

0110In embodiments, optimizing the set of vehicle parameters is responsive to an identifying, by the genetic algorithm <b>975</b>, of at least one vehicle parameter that produces a favorable rider state. In embodiments, the genetic algorithm <b>975</b> is to optimize the set of vehicle parameters for vehicle performance. In embodiments, the genetic algorithm <b>975</b> is to optimize the set of vehicle parameters for the state of the rider and is to optimize the set of vehicle parameters for vehicle performance. In embodiments, optimizing the set of vehicle parameters is responsive to the genetic algorithm <b>975</b> identifying at least one of a favorable vehicle operating state, and favorable vehicle performance that maintains the rider state <b>937</b>. In embodiments, the artificial intelligence system <b>936</b> further includes a neural network selected from a plurality of different neural networks. In embodiments, the selection of the neural network involves the genetic algorithm <b>975</b>. In embodiments, the selection of the neural network is based on a structured competition among the plurality of different neural networks. In embodiments, the genetic algorithm <b>975</b> facilitates training a neural network to process interactions among a plurality of vehicle operating systems and riders to produce the optimized set of vehicle parameters.

0111In embodiments, a set of inputs relating to at least one vehicle parameter are provided by at least one of an on-board diagnostic system, a telemetry system, a vehicle-located sensor, and a system external to the vehicle. In embodiments, the inputs representative of the rider state <b>937</b> comprise at least one of comfort, emotional state, satisfaction, goals, classification of trip, or fatigue. In embodiments, the inputs representative of the rider state <b>937</b> reflect a satisfaction parameter of at least one of a driver, a fleet manager, an advertiser, a merchant, an owner, an operator, an insurer, and a regulator. In embodiments, the inputs representative of the rider state <b>937</b> comprise inputs relating to a user that, when processed with a cognitive system yield the rider state <b>937</b>.

0112Referring to <figref idref="DRAWINGS">FIG. <b>10</b></figref>, in embodiments provided herein are transportation systems <b>1011</b> having a hybrid neural network <b>1047</b> for optimizing the operating state of a continuously variable powertrain <b>1013</b> of a vehicle <b>1010</b>. In embodiments, at least one part of the hybrid neural network <b>1047</b> operates to classify a state of the vehicle <b>1010</b> and another part of the hybrid neural network <b>1047</b> operates to optimize at least one operating parameter <b>1060</b> of the transmission <b>1019</b>. In embodiments, the vehicle <b>1010</b> may be a self-driving vehicle. In an example, the first portion <b>1085</b> of the hybrid neural network may classify the vehicle <b>1010</b> as operating in a high-traffic state (such as by use of LIDAR, RADAR, or the like that indicates the presence of other vehicles, or by taking input from a traffic monitoring system, or by detecting the presence of a high density of mobile devices, or the like) and a bad weather state (such as by taking inputs indicating wet roads (such as using vision-based systems), precipitation (such as determined by radar), presence of ice (such as by temperature sensing, vision-based sensing, or the like), hail (such as by impact detection, sound-sensing, or the like), lightning (such as by vision-based systems, sound-based systems, or the like), or the like. Once classified, another neural network <b>1086</b> (optionally of another type) may optimize the vehicle operating parameter based on the classified state, such as by putting the vehicle <b>1010</b> into a safe-driving mode (e.g., by providing forward-sensing alerts at greater distances and/lower speeds than in good weather, by providing automated braking earlier and more aggressively than in good weather, and the like).

0113An aspect provided herein includes a system for transportation <b>1011</b>, comprising: a hybrid neural network <b>1047</b> for optimizing an operating state of a continuously variable powertrain <b>1013</b> of a vehicle <b>1010</b>. In embodiments, a portion <b>1085</b> of the hybrid neural network <b>1047</b> is to operate to classify a state <b>1044</b> of the vehicle <b>1010</b> thereby generating a classified state of the vehicle, and an other portion <b>1086</b> of the hybrid neural network <b>1047</b> is to operate to optimize at least one operating parameter <b>1060</b> of a transmission <b>1019</b> portion of the continuously variable powertrain <b>1013</b>.

0114In embodiments, the system for transportation <b>1011</b> further comprises: an artificial intelligence system <b>1036</b> operative on at least one processor <b>1088</b>, the artificial intelligence system <b>1036</b> to operate the portion <b>1085</b> of the hybrid neural network <b>1047</b> to operate to classify the state of the vehicle and the artificial intelligence system <b>1036</b> to operate the other portion <b>1086</b> of the hybrid neural network <b>1047</b> to optimize the at least one operating parameter <b>1087</b> of the transmission <b>1019</b> portion of the continuously variable powertrain <b>1013</b> based on the classified state of the vehicle. In embodiments, the vehicle <b>1010</b> comprises a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle <b>1010</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>1010</b> is to be automatically routed. In embodiments, the vehicle <b>1010</b> is a self-driving vehicle. In embodiments, the classified state of the vehicle is: a vehicle maintenance state; a vehicle health state; a vehicle operating state; a vehicle energy utilization state; a vehicle charging state; a vehicle satisfaction state; a vehicle component state; a vehicle sub-system state; a vehicle powertrain system state; a vehicle braking system state; a vehicle clutch system state; a vehicle lubrication system state; a vehicle transportation infrastructure system state; or a vehicle rider state. In embodiments, at least a portion of the hybrid neural network <b>1047</b> is a convolutional neural network.

0115<figref idref="DRAWINGS">FIG. <b>11</b></figref> illustrates a method <b>1100</b> for optimizing operation of a continuously variable vehicle powertrain of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At <b>1102</b>, the method includes executing a first network of a hybrid neural network on at least one processor, the first network classifying a plurality of operational states of the vehicle. In embodiments, at least a portion of the operational states is based on a state of the continuously variable powertrain of the vehicle. At <b>1104</b>, the method includes executing a second network of the hybrid neural network on the at least one processor, the second network processing inputs that are descriptive of the vehicle and of at least one detected condition associated with an occupant of the vehicle for at least one of the plurality of classified operational states of the vehicle. In embodiments, the processing the inputs by the second network causes optimization of at least one operating parameter of the continuously variable powertrain of the vehicle for a plurality of the operational states of the vehicle.

0116Referring to <figref idref="DRAWINGS">FIG. <b>10</b></figref> and <figref idref="DRAWINGS">FIG. <b>11</b></figref> together, in embodiments, the vehicle comprises an artificial intelligence system <b>1036</b>, the method further comprising automating at least one control parameter of the vehicle by the artificial intelligence system <b>1036</b>. In embodiments, the vehicle <b>1010</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>1010</b> is to be automatically routed. In embodiments, the vehicle <b>1010</b> is a self-driving vehicle. In embodiments, the method further comprises optimizing, by the artificial intelligence system <b>1036</b>, an operating state of the continuously variable powertrain <b>1013</b> of the vehicle based on the optimized at least one operating parameter <b>1060</b> of the continuously variable powertrain <b>1013</b> by adjusting at least one other operating parameter <b>1087</b> of a transmission <b>1019</b> portion of the continuously variable powertrain <b>1013</b>.

0117In embodiments, the method further comprises optimizing, by the artificial intelligence system <b>1036</b>, the operating state of the continuously variable powertrain <b>1013</b> by processing social data from a plurality of social data sources. In embodiments, the method further comprises optimizing, by the artificial intelligence system <b>1036</b>, the operating state of the continuously variable powertrain <b>1013</b> by processing data sourced from a stream of data from unstructured data sources. In embodiments, the method further comprises optimizing, by the artificial intelligence system <b>1036</b>, the operating state of the continuously variable powertrain <b>1013</b> by processing data sourced from wearable devices. In embodiments, the method further comprises optimizing, by the artificial intelligence system <b>1036</b>, the operating state of the continuously variable powertrain <b>1013</b> by processing data sourced from in-vehicle sensors. In embodiments, the method further comprises optimizing, by the artificial intelligence system <b>1036</b>, the operating state of the continuously variable powertrain <b>1013</b> by processing data sourced from a rider helmet.

0118In embodiments, the method further comprises optimizing, by the artificial intelligence system <b>1036</b>, the operating state of the continuously variable powertrain <b>1013</b> by processing data sourced from rider headgear. In embodiments, the method further comprises optimizing, by the artificial intelligence system <b>1036</b>, the operating state of the continuously variable powertrain <b>1013</b> by processing data sourced from a rider voice system. In embodiments, the method further comprises operating, by the artificial intelligence system <b>1036</b>, a third network of the hybrid neural network <b>1047</b> to predict a state of the vehicle based at least in part on at least one of the classified plurality of operational states of the vehicle and at least one operating parameter of the transmission <b>1019</b>. In embodiments, the first network of the hybrid neural network <b>1047</b> comprises a structure-adaptive network to adapt a structure of the first network responsive to a result of operating the first network of the hybrid neural network <b>1047</b>. In embodiments, the first network of the hybrid neural network <b>1047</b> is to process a plurality of social data from social data sources to classify the plurality of operational states of the vehicle.

0119In embodiments, at least a portion of the hybrid neural network <b>1047</b> is a convolutional neural network. In embodiments, at least one of the classified plurality of operational states of the vehicle is: a vehicle maintenance state; or a vehicle health state. In embodiments, at least one of the classified states of the vehicle is: a vehicle operating state; a vehicle energy utilization state; a vehicle charging state; a vehicle satisfaction state; a vehicle component state; a vehicle sub-system state; a vehicle powertrain system state; a vehicle braking system state; a vehicle clutch system state; a vehicle lubrication system state; or a vehicle transportation infrastructure system state. In embodiments, the at least one of classified states of the vehicle is a vehicle driver state. In embodiments, the at least one of classified states of the vehicle is a vehicle rider state.

0120Referring to <figref idref="DRAWINGS">FIG. <b>12</b></figref>, in embodiments, provided herein are transportation systems <b>1211</b> having a cognitive system for routing at least one vehicle <b>1210</b> within a set of vehicles <b>1294</b> based on a routing parameter determined by facilitating negotiation among a designated set of vehicles. In embodiments, negotiation accepts inputs relating to the value attributed by at least one rider to at least one parameter <b>1230</b> of a route <b>1295</b>. A user <b>1290</b> may express value by a user interface that rates one or more parameters (e.g., any of the parameters noted throughout), by behavior (e.g., undertaking behavior that reflects or indicates value ascribed to arriving on time, following a given route <b>1295</b>, or the like), or by providing or offering value (e.g., offering currency, tokens, points, cryptocurrency, rewards, or the like). For example, a user <b>1290</b> may negotiate for a preferred route by offering tokens to the system that are awarded if the user <b>1290</b> arrives at a designated time, while others may offer to accept tokens in exchange for taking alternative routes (and thereby reducing congestion). Thus, an artificial intelligence system may optimize a combination of offers to provide rewards or to undertake behavior in response to rewards, such that the reward system optimizes a set of outcomes. Negotiation may include explicit negotiation, such as where a driver offers to reward drivers ahead of the driver on the road in exchange for their leaving the route temporarily as the driver passes.

0121An aspect provided herein includes a system for transportation <b>1211</b>, comprising: a cognitive system for routing at least one vehicle <b>1210</b> within a set of vehicles <b>1294</b> based on a routing parameter determined by facilitating a negotiation among a designated set of vehicles, wherein the negotiation accepts inputs relating to a value attributed by at least one user <b>1290</b> to at least one parameter of a route <b>1295</b>.

0122<figref idref="DRAWINGS">FIG. <b>13</b></figref> illustrates a method <b>1300</b> of negotiation-based vehicle routing in accordance with embodiments of the systems and methods disclosed herein. At <b>1302</b>, the method includes facilitating a negotiation of a route-adjustment value for a plurality of parameters used by a vehicle routing system to route at least one vehicle in a set of vehicles. At <b>1304</b>, the method includes determining a parameter in the plurality of parameters for optimizing at least one outcome based on the negotiation.

0123Referring to <figref idref="DRAWINGS">FIG. <b>12</b></figref> and <figref idref="DRAWINGS">FIG. <b>13</b></figref>, in embodiments, a user <b>1290</b> is an administrator for a set of roadways to be used by the at least one vehicle <b>1210</b> in the set of vehicles <b>1294</b>. In embodiments, a user <b>1290</b> is an administrator for a fleet of vehicles including the set of vehicles <b>1294</b>. In embodiments, the method further comprises offering a set of offered user-indicated values for the plurality of parameters <b>1230</b> to users <b>1290</b> with respect to the set of vehicles <b>1294</b>. In embodiments, the route-adjustment value <b>1224</b> is based at least in part on the set of offered user-indicated values <b>1297</b>. In embodiments, the route-adjustment value <b>1224</b> is further based on at least one user response to the offering. In embodiments, the route-adjustment value <b>1224</b> is based at least in part on the set of offered user-indicated values <b>1297</b> and at least one response thereto by at least one user of the set of vehicles <b>1294</b>. In embodiments, the determined parameter facilitates adjusting a route <b>1295</b> of at least one of the vehicles <b>1210</b> in the set of vehicles <b>1294</b>. In embodiments, adjusting the route includes prioritizing the determined parameter for use by the vehicle routing system.

0124In embodiments, the facilitating negotiation includes facilitating negotiation of a price of a service. In embodiments, the facilitating negotiation includes facilitating negotiation of a price of fuel. In embodiments, the facilitating negotiation includes facilitating negotiation of a price of recharging. In embodiments, the facilitating negotiation includes facilitating negotiation of a reward for taking a routing action.

0125An aspect provided herein includes a transportation system <b>1211</b> for negotiation-based vehicle routing comprising: a route adjustment negotiation system <b>1289</b> through which users <b>1290</b> in a set of users <b>1291</b> negotiate a route-adjustment value <b>1224</b> for at least one of a plurality of parameters <b>1230</b> used by a vehicle routing system <b>1292</b> to route at least one vehicle <b>1210</b> in a set of vehicles <b>1294</b>; and a user route optimizing circuit <b>1293</b> to optimize a portion of a route <b>1295</b> of at least one user <b>1290</b> of the set of vehicles <b>1294</b> based on the route-adjustment value <b>1224</b> for the at least one of the plurality of parameters <b>1230</b>. In embodiments, the route-adjustment value <b>1224</b> is based at least in part on user-indicated values <b>1297</b> and at least one negotiation response thereto by at least one user of the set of vehicles <b>1294</b>. In embodiments, the transportation system <b>1211</b> further comprises a vehicle-based route negotiation interface through which user-indicated values <b>1297</b> for the plurality of parameters <b>1230</b> used by the vehicle routing system are captured. In embodiments, a user <b>1290</b> is a rider of the at least one vehicle <b>1210</b>. In embodiments, a user <b>1290</b> is an administrator for a set of roadways to be used by the at least one vehicle <b>1210</b> in the set of vehicles <b>1294</b>.

0126In embodiments, a user <b>1290</b> is an administrator for a fleet of vehicles including the set of vehicles <b>1294</b>. In embodiments, the at least one of the plurality of parameters <b>1230</b> facilitates adjusting a route <b>1295</b> of the at least one vehicle <b>1210</b>. In embodiments, adjusting the route <b>1295</b> includes prioritizing a determined parameter for use by the vehicle routing system. In embodiments, at least one of the user-indicated values <b>1297</b> is attributed to at least one of the plurality of parameters <b>1230</b> through an interface to facilitate expression of rating one or more route parameters. In embodiments, the vehicle-based route negotiation interface facilitates expression of rating one or more route parameters. In embodiments, the user-indicated values <b>1297</b> are derived from a behavior of the user <b>1290</b>. In embodiments, the vehicle-based route negotiation interface facilitates converting user behavior to the user-indicated values <b>1297</b>. In embodiments, the user behavior reflects value ascribed to the at least one parameter used by the vehicle routing system to influence a route <b>1295</b> of at least one vehicle <b>1210</b> in the set of vehicles <b>1294</b>. In embodiments, the user-indicated value indicated by at least one user <b>1290</b> correlates to an item of value provided by the user <b>1290</b>. In embodiments, the item of value is provided by the user <b>1290</b> through an offering of the item of value in exchange for a result of routing based on the at least one parameter. In embodiments, the negotiating of the route-adjustment value <b>1224</b> includes offering an item of value to the users of the set of vehicles <b>1294</b>.

0127Referring to <figref idref="DRAWINGS">FIG. <b>14</b></figref>, in embodiments provided herein are transportation systems <b>1411</b> having a cognitive system for routing at least one vehicle <b>1410</b> within a set of vehicles <b>1494</b> based on a routing parameter determined by facilitating coordination among a designated set of vehicles <b>1498</b>. In embodiments, the coordination is accomplished by taking at least one input from at least one game-based interface <b>1499</b> for riders of the vehicles. A game-based interface <b>1499</b> may include rewards for undertaking game-like actions (i.e., game activities <b>14101</b>) that provide an ancillary benefit. For example, a rider in a vehicle <b>1410</b> may be rewarded for routing the vehicle <b>1410</b> to a point of interest off a highway (such as to collect a coin, to capture an item, or the like), while the rider's departure clears space for other vehicles that are seeking to achieve other objectives, such as on-time arrival. For example, a game like Pokemon Go™ may be configured to indicate the presence of rare Pokemon™ creatures in locations that attract traffic away from congested locations. Others may provide rewards (e.g., currency, cryptocurrency or the like) that may be pooled to attract users <b>1490</b> away from congested roads.

0128An aspect provided herein includes a system for transportation <b>1411</b>, comprising: a cognitive system for routing at least one vehicle <b>1410</b> within a set of vehicles <b>1494</b> based on a set of routing parameters <b>1430</b> determined by facilitating coordination among a designated set of vehicles <b>1498</b>, wherein the coordination is accomplished by taking at least one input from at least one game-based interface <b>1499</b> for a user <b>1490</b> of a vehicle <b>1410</b> in the designated set of vehicles <b>1498</b>.

0129In embodiments, the system for transportation further comprises: a vehicle routing system <b>1492</b> to route the at least one vehicle <b>1410</b> based on the set of routing parameters <b>1430</b>; and the game-based interface <b>1499</b> through which the user <b>1490</b> indicates a routing preference <b>14100</b> for at least one vehicle <b>1410</b> within the set of vehicles <b>1494</b> to undertake a game activity <b>14101</b> offered in the game-based interface <b>1499</b>; wherein the game-based interface <b>1499</b> is to induce the user <b>1490</b> to undertake a set of favorable routing choices based on the set of routing parameters <b>1430</b>. As used herein, “to route” means to select a route <b>1495</b>.

0130In embodiments, the vehicle routing system <b>1492</b> accounts for the routing preference <b>14100</b> of the user <b>1490</b> when routing the at least one vehicle <b>1410</b> within the set of vehicles <b>1494</b>. In embodiments, the game-based interface <b>1499</b> is disposed for in-vehicle use as indicated in <figref idref="DRAWINGS">FIG. <b>14</b></figref> by the line extending from the Game-Based Interface into the box for Vehicle <b>1</b>. In embodiments, the user <b>1490</b> is a rider of the at least one vehicle <b>1410</b>. In embodiments, the user <b>1490</b> is an administrator for a set of roadways to be used by the at least one vehicle <b>1410</b> in the set of vehicles <b>1494</b>. In embodiments, the user <b>1490</b> is an administrator for a fleet of vehicles including the set of vehicles <b>1494</b>. In embodiments, the set of routing parameters <b>1430</b> includes at least one of traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high-crime regions, collective satisfaction, maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, avoidance of driver-operated vehicles. In embodiments, the game activity <b>14101</b> offered in the game-based interface <b>1499</b> includes contests. In embodiments, the game activity <b>14101</b> offered in the game-based interface <b>1499</b> includes entertainment games.

0131In embodiments, the game activity <b>14101</b> offered in the game-based interface <b>1499</b> includes competitive games. In embodiments, the game activity <b>14101</b> offered in the game-based interface <b>1499</b> includes strategy games. In embodiments, the game activity <b>14101</b> offered in the game-based interface <b>1499</b> includes scavenger hunts. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves a fuel efficiency objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves a reduced traffic objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves a reduced pollution objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves a reduced carbon footprint objective.

0132In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves a reduced noise in neighborhoods objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves a collective satisfaction objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves an avoiding accident scenes objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves an avoiding high-crime areas objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves a reduced traffic congestion objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves a bad weather avoidance objective.

0133In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves a maximum travel time objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves a maximum speed limit objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves an avoidance of toll roads objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves an avoidance of city roads objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves an avoidance of undivided highways objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves an avoidance of left turns objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system <b>1492</b> achieves an avoidance of driver-operated vehicles objective.

0134<figref idref="DRAWINGS">FIG. <b>15</b></figref> illustrates a method <b>1500</b> of game-based coordinated vehicle routing in accordance with embodiments of the systems and methods disclosed herein. At <b>1502</b>, the method includes presenting, in a game-based interface, a vehicle route preference-affecting game activity. At <b>1504</b>, the method includes receiving, through the game-based interface, a user response to the presented game activity. At <b>1506</b>, the method includes adjusting a routing preference for the user responsive to the received response. At <b>1508</b>, the method includes determining at least one vehicle-routing parameter used to route vehicles to reflect the adjusted routing preference for routing vehicles. At <b>1509</b>, the method includes routing, with a vehicle routing system, vehicles in a set of vehicles responsive to the at least one determined vehicle routing parameter adjusted to reflect the adjusted routing preference, wherein routing of the vehicles includes adjusting the determined routing parameter for at least a plurality of vehicles in the set of vehicles.

0135Referring to <figref idref="DRAWINGS">FIG. <b>14</b></figref> and <figref idref="DRAWINGS">FIG. <b>15</b></figref>, in embodiments, the method further comprises indicating, by the game-based interface <b>1499</b>, a reward value <b>14102</b> for accepting the game activity <b>14101</b>. In embodiments, the game-based interface <b>1499</b> further comprises a routing preference negotiation system <b>1436</b> for a rider to negotiate the reward value <b>14102</b> for accepting the game activity <b>14101</b>. In embodiments, the reward value <b>14102</b> is a result of pooling contributions of value from riders in the set of vehicles. In embodiments, at least one routing parameter <b>1430</b> used by the vehicle routing system <b>1492</b> to route the vehicles <b>1410</b> in the set of vehicles <b>1494</b> is associated with the game activity <b>14101</b> and a user acceptance of the game activity <b>14101</b> adjusts (e.g., by the routing adjustment value <b>1424</b>) the at least one routing parameter <b>1430</b> to reflect the routing preference. In embodiments, the user response to the presented game activity <b>14101</b> is derived from a user interaction with the game-based interface <b>1499</b>. In embodiments, the at least one routing parameter used by the vehicle routing system <b>1492</b> to route the vehicles <b>1410</b> in the set of vehicles <b>1494</b> includes at least one of: traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high-crime regions, collective satisfaction, maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, and avoidance of driver-operated vehicles.

0136In embodiments, the game activity <b>14101</b> presented in the game-based interface <b>1499</b> includes contests. In embodiments, the game activity <b>14101</b> presented in the game-based interface <b>1499</b> includes entertainment games. In embodiments, the game activity <b>14101</b> presented in the game-based interface <b>1496</b> includes competitive games. In embodiments, the game activity <b>14101</b> presented in the game-based interface <b>1499</b> includes strategy games. In embodiments, the game activity <b>14101</b> presented in the game-based interface <b>1499</b> includes scavenger hunts. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves a fuel efficiency objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves a reduced traffic objective.

0137In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves a reduced pollution objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves a reduced carbon footprint objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves a reduced noise in neighborhoods objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves a collective satisfaction objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves an avoiding accident scenes objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves an avoiding high-crime areas objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves a reduced traffic congestion objective.

0138In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves a bad weather avoidance objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves a maximum travel time objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves a maximum speed limit objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves an avoidance of toll roads objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves an avoidance of city roads objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves an avoidance of undivided highways objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves an avoidance of left turns objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter <b>14103</b> achieves an avoidance of driver-operated vehicles objective.

0139In embodiments, provided herein are transportation systems <b>1611</b> having a cognitive system for routing at least one vehicle, wherein the routing is determined at least in part by processing at least one input from a rider interface wherein a rider can obtain a reward <b>16102</b> by undertaking an action while in the vehicle. In embodiments, the rider interface may display a set of available rewards for undertaking various actions, such that the rider may select (such as by interacting with a touch screen or audio interface), a set of rewards to pursue, such as by allowing a navigation system of the vehicle (or of a ride-share system of which the user <b>1690</b> has at least partial control) or a routing system <b>1692</b> of a self-driving vehicle to use the actions that result in rewards to govern routing. For example, selection of a reward for attending a site may result in sending a signal to a navigation or routing system <b>1692</b> to set an intermediate destination at the site. As another example, indicating a willingness to watch a piece of content may cause a routing system <b>1692</b> to select a route that permits adequate time to view or hear the content.

0140An aspect provided herein includes a system for transportation <b>1611</b>, comprising: a cognitive system for routing at least one vehicle <b>1610</b>, wherein the routing is based, at least in part, by processing at least one input from a rider interface, wherein a reward <b>16102</b> is made available to a rider in response to the rider undertaking a predetermined action while in the at least one vehicle <b>1610</b>.

0141An aspect provided herein includes a transportation system <b>1611</b> for reward-based coordinated vehicle routing comprising: a reward-based interface <b>16104</b> to offer a reward <b>16102</b> and through which a user <b>1690</b> related to a set of vehicles <b>1694</b> indicates a routing preference of the user <b>1690</b> related to the reward <b>16102</b> by responding to the reward <b>16102</b> offered in the reward-based interface <b>16104</b>; a reward offer response processing circuit <b>16105</b> to determine at least one user action resulting from the user response to the reward <b>16102</b> and to determine a corresponding effect <b>16106</b> on at least one routing parameter <b>1630</b>; and a vehicle routing system <b>1692</b> to use the routing preference <b>16100</b> of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles <b>1694</b>.

0142In embodiments, the user <b>1690</b> is a rider of at least one vehicle <b>1610</b> in the set of vehicles <b>1694</b>. In embodiments, the user <b>1690</b> is an administrator for a set of roadways to be used by at least one vehicle <b>1610</b> in the set of vehicles <b>1694</b>. In embodiments, the user <b>1690</b> is an administrator for a fleet of vehicles including the set of vehicles <b>1694</b>. In embodiments, the reward-based interface <b>16104</b> is disposed for in-vehicle use. In embodiments, the at least one routing parameter <b>1630</b> includes at least one of: traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high-crime regions, collective satisfaction, maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, and avoidance of driver-operated vehicles. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a fuel efficiency objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced traffic objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve′ a reduced pollution objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced carbon footprint objective.

0143In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced noise in neighborhoods objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a collective satisfaction objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve′ an avoiding accident scenes objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoiding high-crime areas objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced traffic congestion objective.

0144In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a bad weather avoidance objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a maximum travel time objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a maximum speed limit objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of toll roads objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of city roads objective.

0145In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of undivided highways objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of left turns objective. In embodiments, the vehicle routing system <b>1692</b> is to use the routing preference of the user <b>1690</b> and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of driver-operated vehicles objective.

0146<figref idref="DRAWINGS">FIG. <b>17</b></figref> illustrates a method <b>1700</b> of reward-based coordinated vehicle routing in accordance with embodiments of the systems and methods disclosed herein. At <b>1702</b>, the method includes receiving through a reward-based interface a response of a user related to a set of vehicles to a reward offered in the reward-based interface. At <b>1704</b>, the method includes determining a routing preference based on the response of the user. At <b>1706</b>, the method includes determining at least one user action resulting from the response of the user to the reward. At <b>1708</b>, the method includes determining a corresponding effect of the at least one user action on at least one routing parameter. At <b>1709</b>, the method includes governing routing of the set of vehicles responsive to the routing preference and the corresponding effect on the at least one routing parameter.

0147In embodiments, the user <b>1690</b> is a rider of at least one vehicle <b>1610</b> in the set of vehicles <b>1694</b>. In embodiments, the user <b>1690</b> is an administrator for a set of roadways to be used by at least one vehicle <b>1610</b> in the set of vehicles <b>1694</b>. In embodiments, the user <b>1690</b> is an administrator for a fleet of vehicles including the set of vehicles <b>1694</b>.

0148In embodiments, the reward-based interface <b>16104</b> is disposed for in-vehicle use. In embodiments, the at least one routing parameter <b>1630</b> includes at least one of: traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high-crime regions, collective satisfaction, maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, and avoidance of driver-operated vehicles. In embodiments, the user <b>1690</b> responds to the reward <b>16102</b> offered in the reward-based interface <b>16104</b> by accepting the reward <b>16102</b> offered in the interface, rejecting the reward <b>16102</b> offered in the reward-based interface <b>16104</b>, or ignoring the reward <b>16102</b> offered in the reward-based interface <b>16104</b>. In embodiments, the user <b>1690</b> indicates the routing preference by either accepting or rejecting the reward <b>16102</b> offered in the reward-based interface <b>16104</b>. In embodiments, the user <b>1690</b> indicates the routing preference by undertaking an action in at least one vehicle <b>1610</b> in the set of vehicles <b>1694</b> that facilitates transferring the reward <b>16102</b> to the user <b>1690</b>.

0149In embodiments, the method further comprises sending, via a reward offer response processing circuit <b>16105</b>, a signal to the vehicle routing system <b>1692</b> to select a vehicle route that permits adequate time for the user <b>1690</b> to perform the at least one user action. In embodiments, the method further comprises: sending, via a reward offer response processing circuit <b>16105</b>, a signal to a vehicle routing system <b>1692</b>, the signal indicating a destination of a vehicle associated with the at least one user action; and adjusting, by the vehicle routing system <b>1692</b>, a route of the vehicle <b>1695</b> associated with the at least one user action to include the destination. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing fuel efficiency objective.

0150In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing reduced traffic objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing reduced pollution objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing reduced carbon footprint objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing reduced noise in neighborhoods objective.

0151In embodiments, reward <b>16102</b> is associated with achieving a vehicle routing collective satisfaction objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing avoiding accident scenes objective.

0152In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing avoiding high-crime areas objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing reduced traffic congestion objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing bad weather avoidance objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing maximum travel time objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing maximum speed limit objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing avoidance of toll roads objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing avoidance of city roads objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing avoidance of undivided highways objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing avoidance of left turns objective. In embodiments, the reward <b>16102</b> is associated with achieving a vehicle routing avoidance of driver-operated vehicles objective.

0153Referring to <figref idref="DRAWINGS">FIG. <b>18</b></figref>, in embodiments provided herein are transportation systems <b>1811</b> having a data processing system <b>1862</b> for taking data <b>18114</b> from a plurality <b>1869</b> of social data sources <b>18107</b> and using a neural network <b>18108</b> to predict an emerging transportation need <b>18112</b> for a group of individuals. Among the various social data sources <b>18107</b>, such as those described above, a large amount of data is available relating to social groups, such as friend groups, families, workplace colleagues, club members, people having shared interests or affiliations, political groups, and others. The expert system described above can be trained, as described throughout, such as using a training data set of human predictions and/or a model, with feedback of outcomes, to predict the transportation needs of a group. For example, based on a discussion thread of a social group as indicated at least in part on a social network feed, it may become evident that a group meeting or trip will take place, and the system may (such as using location information for respective members, as well as indicators of a set of destinations of the trip), predict where and when each member would need to travel in order to participate. Based on such a prediction, the system could automatically identify and show options for travel, such as available public transportation options, flight options, ride share options, and the like. Such options may include ones by which the group may share transportation, such as indicating a route that results in picking up a set of members of the group for travel together. Social media information may include posts, tweets, comments, chats, photographs, and the like and may be processed as noted above.

0154An aspect provided herein includes a system <b>1811</b> for transportation, comprising: a data processing system <b>1862</b> for taking data <b>18114</b> from a plurality <b>1869</b> of social data sources <b>18107</b> and using a neural network <b>18108</b> to predict an emerging transportation need <b>18112</b> for a group of individuals <b>18110</b>.

0155<figref idref="DRAWINGS">FIG. <b>19</b></figref> illustrates a method <b>1900</b> of predicting a common transportation need for a group in accordance with embodiments of the systems and methods disclosed herein. At <b>1902</b>, the method includes gathering social media-sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources. At <b>1904</b>, the method includes processing the data to identify a subset of the plurality of individuals who form a social group based on group affiliation references in the data. At <b>1906</b>, the method includes detecting keywords in the data indicative of a transportation need. At <b>1908</b>, the method includes using a neural network trained to predict transportation needs based on the detected keywords to identify the common transportation need for the subset of the plurality of individuals.

0156Referring to <figref idref="DRAWINGS">FIG. <b>18</b></figref> and <figref idref="DRAWINGS">FIG. <b>19</b></figref>, in embodiments, the neural network <b>18108</b> is a convolutional neural network <b>18113</b>. In embodiments, the neural network <b>18108</b> is trained based on a model that facilitates matching phrases in social media with transportation activity. In embodiments, the neural network <b>18108</b> predicts at least one of a destination and an arrival time for the subset <b>18110</b> of the plurality of individuals sharing the common transportation need. In embodiments, the neural network <b>18108</b> predicts the common transportation need based on analysis of transportation need-indicative keywords detected in a discussion thread among a portion of individuals in the social group. In embodiments, the method further comprises identifying at least one shared transportation service <b>18111</b> that facilitates a portion of the social group meeting the predicted common transportation need <b>18112</b>. In embodiments, the at least one shared transportation service comprises generating a vehicle route that facilitates picking up the portion of the social group.

0157<figref idref="DRAWINGS">FIG. <b>20</b></figref> illustrates a method <b>2000</b> of predicting a group transportation need for a group in accordance with embodiments of the systems and methods disclosed herein. At <b>2002</b>, the method includes gathering social media-sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources. At <b>2004</b>, the method includes processing the data to identify a subset of the plurality of individuals who share the group transportation need. At <b>2006</b>, the method includes detecting keywords in the data indicative of the group transportation need for the subset of the plurality of individuals. At <b>2008</b>, the method includes predicting the group transportation need using a neural network trained to predict transportation needs based on the detected keywords. At <b>2009</b>, the method includes directing a vehicle routing system to meet the group transportation need.

0158Referring to <figref idref="DRAWINGS">FIG. <b>18</b></figref> and <figref idref="DRAWINGS">FIG. <b>20</b></figref>, in embodiments, the neural network <b>18108</b> is a convolutional neural network <b>18113</b>. In embodiments, directing the vehicle routing system to meet the group transportation need involves routing a plurality of vehicles to a destination derived from the social media-sourced data <b>18114</b>. In embodiments, the neural network <b>18108</b> is trained based on a model that facilitates matching phrases in the social media-sourced data <b>18114</b> with transportation activities. In embodiments, the method further comprises predicting, by the neural network <b>18108</b>, at least one of a destination and an arrival time for the subset <b>18110</b> of the plurality <b>18109</b> of individuals sharing the group transportation need. In embodiments, the method further comprises predicting, by the neural network <b>18108</b>, the group transportation need based on an analysis of transportation need-indicative keywords detected in a discussion thread in the social media-sourced data <b>18114</b>. In embodiments, the method further comprises identifying at least one shared transportation service <b>18111</b> that facilitates meeting the predicted group transportation need for at least a portion of the subset <b>18110</b> of the plurality of individuals. In embodiments, the at least one shared transportation service <b>18111</b> comprises generating a vehicle route that facilitates picking up the at least the portion of the subset <b>18110</b> of the plurality of individuals.

0159<figref idref="DRAWINGS">FIG. <b>21</b></figref> illustrates a method <b>2100</b> of predicting a group transportation need in accordance with embodiments of the systems and methods disclosed herein. At <b>2102</b>, the method includes gathering social media-sourced data from a plurality of social media sources. At <b>2104</b>, the method includes processing the data to identify an event. At <b>2106</b>, the method includes detecting keywords in the data indicative of the event to determine a transportation need associated with the event. At <b>2108</b>, the method includes using a neural network trained to predict transportation needs based at least in part on social media-sourced data to direct a vehicle routing system to meet the transportation need.

0160Referring to <figref idref="DRAWINGS">FIG. <b>18</b></figref> and <figref idref="DRAWINGS">FIG. <b>21</b></figref>, in embodiments, the neural network <b>18108</b> is a convolutional neural network <b>18113</b>. In embodiments, the vehicle routing system is directed to meet the transportation need by routing a plurality of vehicles to a location associated with the event. In embodiments, the vehicle routing system is directed to meet the transportation need by routing a plurality of vehicles to avoid a region proximal to a location associated with the event. In embodiments, the vehicle routing system is directed to meet the transportation need by routing vehicles associated with users whose social media-sourced data <b>18114</b> do not indicate the transportation need to avoid a region proximal to a location associated with the event. In embodiments, the method further comprises presenting at least one transportation service for satisfying the transportation need. In embodiments, the neural network <b>18108</b> is trained based on a model that facilitates matching phrases in social media-sourced data <b>18114</b> with transportation activity.

0161In embodiments, the neural network <b>18108</b> predicts at least one of a destination and an arrival time for individuals attending the event. In embodiments, the neural network <b>18108</b> predicts the transportation need based on analysis of transportation need-indicative keywords detected in a discussion thread in the social media-sourced data <b>18114</b>. In embodiments, the method further comprises identifying at least one shared transportation service that facilitates meeting the predicted transportation need for at least a subset of individuals identified in the social media-sourced data <b>18114</b>. In embodiments, the at least one shared transportation service comprises generating a vehicle route that facilitates picking up the portion of the subset of individuals identified in the social media-sourced data <b>18114</b>.

0162Referring to <figref idref="DRAWINGS">FIG. <b>22</b></figref>, in embodiments provided herein are transportation systems <b>2211</b> having a data processing system <b>2211</b> for taking social media data <b>22114</b> from a plurality <b>2269</b> of social data sources <b>22107</b> and using a hybrid neural network <b>2247</b> to optimize an operating state of a transportation system <b>22111</b> based on processing the social data sources <b>22107</b> with the hybrid neural network <b>2247</b>. A hybrid neural network <b>2247</b> may have, for example, a neural network component that makes a classification or prediction based on processing social media data <b>22114</b> (such as predicting a high level of attendance of an event by processing images on many social media feeds that indicate interest in the event by many people, prediction of traffic, classification of interest by an individual in a topic, and many others) and another component that optimizes an operating state of a transportation system, such as an in-vehicle state, a routing state (for an individual vehicle <b>2210</b> or a set of vehicles <b>2294</b>), a user-experience state, or other state described throughout this disclosure (e.g., routing an individual early to a venue like a music festival where there is likely to be very high attendance, playing music content in a vehicle <b>2210</b> for bands who will be at the music festival, or the like).

0163An aspect provided herein includes a system for transportation, comprising: a data processing system <b>2211</b> for taking social media data <b>22114</b> from a plurality <b>2269</b> of social data sources <b>22107</b> and using a hybrid neural network <b>2247</b> to optimize an operating state of a transportation system based on processing the data <b>22114</b> from the plurality <b>2269</b> of social data sources <b>22107</b> with the hybrid neural network <b>2247</b>.

0164An aspect provided herein includes a hybrid neural network system <b>22115</b> for transportation system optimization, the hybrid neural network system <b>22115</b> comprising a hybrid neural network <b>2247</b>, including: a first neural network <b>2222</b> that predicts a localized effect <b>22116</b> on a transportation system through analysis of social medial data <b>22114</b> sourced from a plurality <b>2269</b> of social media data sources <b>22107</b>; and a second neural network <b>2220</b> that optimizes an operating state of the transportation system based on the predicted localized effect <b>22116</b>.

0165In embodiments, at least one of the first neural network <b>2222</b> and the second neural network <b>2220</b> is a convolutional neural network. In embodiments, the second neural network <b>2220</b> is to optimize an in-vehicle rider experience state. In embodiments, the first neural network <b>2222</b> identifies a set of vehicles <b>2294</b> contributing to the localized effect <b>22116</b> based on correlation of vehicle location and an area of the localized effect <b>22116</b>. In embodiments, the second neural network <b>2220</b> is to optimize a routing state of the transportation system for vehicles proximal to a location of the localized effect <b>22116</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of the predicting and optimizing based on keywords in the social media data indicative of an outcome of a transportation system optimization action. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on social media posts.

0166In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on social media feeds. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on ratings derived from the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on like or dislike activity detected in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on indications of relationships in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on user behavior detected in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on discussion threads in the social media data <b>22114</b>.

0167In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on chats in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on photographs in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on traffic-affecting information in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on an indication of a specific individual at a location in the social media data <b>22114</b>. In embodiments, the specific individual is a celebrity. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based a presence of a rare or transient phenomena at a location in the social media data <b>22114</b>.

0168In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based a commerce-related event at a location in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based an entertainment event at a location in the social media data <b>22114</b>. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes traffic conditions. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes weather conditions. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes entertainment options.

0169In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes risk-related conditions. In embodiments, the risk-related conditions include crowds gathering for potentially dangerous reasons. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes commerce-related conditions. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes goal-related conditions.

0170In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes estimates of attendance at an event. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes predictions of attendance at an event. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes modes of transportation. In embodiments, the modes of transportation include car traffic. In embodiments, the modes of transportation include public transportation options.

0171In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes hash tags. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes trending of topics. In embodiments, an outcome of a transportation system optimization action is reducing fuel consumption. In embodiments, an outcome of a transportation system optimization action is reducing traffic congestion. In embodiments, an outcome of a transportation system optimization action is reduced pollution. In embodiments, an outcome of a transportation system optimization action is bad weather avoidance. In embodiments, an operating state of the transportation system being optimized includes an in-vehicle state. In embodiments, an operating state of the transportation system being optimized includes a routing state.

0172In embodiments, the routing state is for an individual vehicle <b>2210</b>. In embodiments, the routing state is for a set of vehicles <b>2294</b>. In embodiments, an operating state of the transportation system being optimized includes a user-experience state.

0173<figref idref="DRAWINGS">FIG. <b>23</b></figref> illustrates a method <b>2300</b> of optimizing an operating state of a transportation system in accordance with embodiments of the systems and methods disclosed herein. At <b>2302</b> the method includes gathering social media-sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources. At <b>2304</b> the method includes optimizing, using a hybrid neural network, the operating state of the transportation system. At <b>2306</b> the method includes predicting, by a first neural network of the hybrid neural network, an effect on the transportation system through an analysis of the social media-sourced data. At <b>2308</b> the method includes optimizing, by a second neural network of the hybrid neural network, at least one operating state of the transportation system responsive to the predicted effect thereon.

0174Referring to <figref idref="DRAWINGS">FIG. <b>22</b></figref> and <figref idref="DRAWINGS">FIG. <b>23</b></figref>, in embodiments, at least one of the first neural network <b>2222</b> and the second neural network <b>2220</b> is a convolutional neural network. In embodiments, the second neural network <b>2220</b> optimizes an in-vehicle rider experience state. In embodiments, the first neural network <b>2222</b> identifies a set of vehicles contributing to the effect based on correlation of vehicle location and an effect area. In embodiments, the second neural network <b>2220</b> optimizes a routing state of the transportation system for vehicles proximal to a location of the effect.

0175In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of the predicting and optimizing based on keywords in the social media data indicative of an outcome of a transportation system optimization action. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on social media posts. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on social media feeds. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on ratings derived from the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on like or dislike activity detected in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on indications of relationships in the social media data <b>22114</b>.

0176In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on user behavior detected in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on discussion threads in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on chats in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on photographs in the social media data <b>22114</b>. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on traffic-affecting information in the social media data <b>22114</b>.

0177In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based on an indication of a specific individual at a location in the social media data. In embodiments, the specific individual is a celebrity. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based a presence of a rare or transient phenomena at a location in the social media data. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based a commerce-related event at a location in the social media data. In embodiments, the hybrid neural network <b>2247</b> is trained for at least one of predicting and optimizing based an entertainment event at a location in the social media data. In embodiments, the social media data analyzed to predict an effect on a transportation system includes traffic conditions.

0178In embodiments, the social media data analyzed to predict an effect on a transportation system includes weather conditions. In embodiments, the social media data analyzed to predict an effect on a transportation system includes entertainment options. In embodiments, the social media data analyzed to predict an effect on a transportation system includes risk-related conditions. In embodiments, the risk-related conditions include crowds gathering for potentially dangerous reasons. In embodiments, the social media data analyzed to predict an effect on a transportation system includes commerce-related conditions. In embodiments, the social media data analyzed to predict an effect on a transportation system includes goal-related conditions.

0179In embodiments, the social media data analyzed to predict an effect on a transportation system includes estimates of attendance at an event. In embodiments, the social media data analyzed to predict an effect on a transportation system includes predictions of attendance at an event. In embodiments, the social media data analyzed to predict an effect on a transportation system includes modes of transportation. In embodiments, the modes of transportation include car traffic. In embodiments, the modes of transportation include public transportation options. In embodiments, the social media data analyzed to predict an effect on a transportation system includes hash tags. In embodiments, the social media data analyzed to predict an effect on a transportation system includes trending of topics.

0180In embodiments, an outcome of a transportation system optimization action is reducing fuel consumption. In embodiments, an outcome of a transportation system optimization action is reducing traffic congestion. In embodiments, an outcome of a transportation system optimization action is reduced pollution. In embodiments, an outcome of a transportation system optimization action is bad weather avoidance. In embodiments, the operating state of the transportation system being optimized includes an in-vehicle state. In embodiments, the operating state of the transportation system being optimized includes a routing state. In embodiments, the routing state is for an individual vehicle. In embodiments, the routing state is for a set of vehicles. In embodiments, the operating state of the transportation system being optimized includes a user-experience state.

0181<figref idref="DRAWINGS">FIG. <b>24</b></figref> illustrates a method <b>2400</b> of optimizing an operating state of a transportation system in accordance with embodiments of the systems and methods disclosed herein. At <b>2402</b> the method includes using a first neural network of a hybrid neural network to classify social media data sourced from a plurality of social media sources as affecting a transportation system. At <b>2404</b> the method includes using a second network of the hybrid neural network to predict at least one operating objective of the transportation system based on the classified social media data. At <b>2406</b> the method includes using a third network of the hybrid neural network to optimize the operating state of the transportation system to achieve the at least one operating objective of the transportation system.

0182Referring to <figref idref="DRAWINGS">FIG. <b>22</b></figref> and <figref idref="DRAWINGS">FIG. <b>24</b></figref>, in embodiments, at least one of the neural networks in the hybrid neural network <b>2247</b> is a convolutional neural network.

0183Referring to <figref idref="DRAWINGS">FIG. <b>25</b></figref>, in embodiments provided herein are transportation systems <b>2511</b> having a data processing system <b>2562</b> for taking social media data <b>25114</b> from a plurality of social data sources <b>25107</b> and using a hybrid neural network <b>2547</b> to optimize an operating state <b>2545</b> of a vehicle <b>2510</b> based on processing the social data sources with the hybrid neural network <b>2547</b>. In embodiments, the hybrid neural network <b>2547</b> can include one neural network category for prediction, another for classification, and another for optimization of one or more operating states, such as based on optimizing one or more desired outcomes (such a providing efficient travel, highly satisfying rider experiences, comfortable rides, on-time arrival, or the like). Social data sources <b>2569</b> may be used by distinct neural network categories (such as any of the types described herein) to predict travel times, to classify content such as for profiling interests of a user, to predict objectives for a transportation plan (such as what will provide overall satisfaction for an individual or a group) and the like. Social data sources <b>2569</b> may also inform optimization, such as by providing indications of successful outcomes (e.g., a social data source <b>25107</b> like a Facebook feed might indicate that a trip was “amazing” or “horrible,” a Yelp review might indicate a restaurant was terrible, or the like). Thus, social data sources <b>2569</b>, by contributing to outcome tracking, can be used to train a system to optimize transportation plans, such as relating to timing, destinations, trip purposes, what individuals should be invited, what entertainment options should be selected, and many others.

0184An aspect provided herein includes a system for transportation <b>2511</b>, comprising: a data processing system <b>2562</b> for taking social media data <b>25114</b> from a plurality of social data sources <b>25107</b> and using a hybrid neural network <b>2547</b> to optimize an operating state <b>2545</b> of a vehicle <b>2510</b> based on processing the data <b>25114</b> from the plurality of social data sources <b>25107</b> with the hybrid neural network <b>2547</b>.

0185<figref idref="DRAWINGS">FIG. <b>26</b></figref> illustrates a method <b>2600</b> of optimizing an operating state of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At <b>2602</b> the method includes classifying, using a first neural network <b>2522</b> (<figref idref="DRAWINGS">FIG. <b>25</b></figref>) of a hybrid neural network, social media data <b>25119</b> (<figref idref="DRAWINGS">FIG. <b>25</b></figref>) sourced from a plurality of social media sources as affecting a transportation system. At <b>2604</b> the method includes predicting, using a second neural network <b>2520</b> (<figref idref="DRAWINGS">FIG. <b>25</b></figref>) of the hybrid neural network, one or more effects <b>25118</b> (<figref idref="DRAWINGS">FIG. <b>25</b></figref>) of the classified social media data on the transportation system. At <b>2606</b> the method includes optimizing, using a third neural network <b>25117</b> (<figref idref="DRAWINGS">FIG. <b>25</b></figref>) of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

0186Referring to <figref idref="DRAWINGS">FIG. <b>25</b></figref> and <figref idref="DRAWINGS">FIG. <b>26</b></figref>, in embodiments, at least one of the neural networks in the hybrid neural network <b>2547</b> is a convolutional neural network. In embodiments, the social media data <b>25114</b> includes social media posts. In embodiments, the social media data <b>25114</b> includes social media feeds. In embodiments, the social media data <b>25114</b> includes like or dislike activity detected in the social media. In embodiments, the social media data <b>25114</b> includes indications of relationships. In embodiments, the social media data <b>25114</b> includes user behavior. In embodiments, the social media data <b>25114</b> includes discussion threads. In embodiments, the social media data <b>25114</b> includes chats. In embodiments, the social media data <b>25114</b> includes photographs.

0187In embodiments, the social media data <b>25114</b> includes traffic-affecting information. In embodiments, the social media data <b>25114</b> includes an indication of a specific individual at a location. In embodiments, the social media data <b>25114</b> includes an indication of a celebrity at a location. In embodiments, the social media data <b>25114</b> includes presence of a rare or transient phenomena at a location. In embodiments, the social media data <b>25114</b> includes a commerce-related event. In embodiments, the social media data <b>25114</b> includes an entertainment event at a location. In embodiments, the social media data <b>25114</b> includes traffic conditions. In embodiments, the social media data <b>25114</b> includes weather conditions. In embodiments, the social media data <b>25114</b> includes entertainment options.

0188In embodiments, the social media data <b>25114</b> includes risk-related conditions. In embodiments, the social media data <b>25114</b> includes predictions of attendance at an event. In embodiments, the social media data <b>25114</b> includes estimates of attendance at an event. In embodiments, the social media data <b>25114</b> includes modes of transportation used with an event. In embodiments, the effect <b>25118</b> on the transportation system includes reducing fuel consumption. In embodiments, the effect <b>25118</b> on the transportation system includes reducing traffic congestion. In embodiments, the effect <b>25118</b> on the transportation system includes reduced carbon footprint. In embodiments, the effect <b>25118</b> on the transportation system includes reduced pollution.

0189In embodiments, the optimized state <b>2544</b> of the at least one vehicle <b>2510</b> is an operating state of the vehicle <b>2545</b>. In embodiments, the optimized state of the at least one vehicle includes an in-vehicle state. In embodiments, the optimized state of the at least one vehicle includes a rider state. In embodiments, the optimized state of the at least one vehicle includes a routing state. In embodiments, the optimized state of the at least one vehicle includes user experience state. In embodiments, a characterization of an outcome of the optimizing in the social media data <b>25114</b> is used as feedback to improve the optimizing. In embodiments, the feedback includes likes and dislikes of the outcome. In embodiments, the feedback includes social medial activity referencing the outcome.

0190In embodiments, the feedback includes trending of social media activity referencing the outcome. In embodiments, the feedback includes hash tags associated with the outcome. In embodiments, the feedback includes ratings of the outcome. In embodiments, the feedback includes requests for the outcome.

0191<figref idref="DRAWINGS">FIG. <b>26</b>A</figref> illustrates a method <b>26</b>A<b>00</b> of optimizing an operating state of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At <b>26</b>A<b>02</b> the method includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. At <b>26</b>A<b>04</b> the method includes predicting, using a second neural network of the hybrid neural network, at least one vehicle-operating objective of the transportation system based on the classified social media data. At <b>26</b>A<b>06</b> the method includes optimizing, using a third neural network of the hybrid neural network, a state of a vehicle in the transportation system to achieve the at least one vehicle-operating objective of the transportation system.

0192Referring to <figref idref="DRAWINGS">FIG. <b>25</b></figref> and <figref idref="DRAWINGS">FIG. <b>26</b>A</figref>, in embodiments, at least one of the neural networks in the hybrid neural network <b>2547</b> is a convolutional neural network. In embodiments, the vehicle-operating objective comprises achieving a rider state of at least one rider in the vehicle. In embodiments, the social media data <b>25114</b> includes social media posts.

0193In embodiments, the social media data <b>25114</b> includes social media feeds. In embodiments, the social media data <b>25114</b> includes like and dislike activity detected in the social media. In embodiments, the social media data <b>25114</b> includes indications of relationships. In embodiments, the social media data <b>25114</b> includes user behavior. In embodiments, the social media data <b>25114</b> includes discussion threads. In embodiments, the social media data <b>25114</b> includes chats. In embodiments, the social media data <b>25114</b> includes photographs. In embodiments, the social media data <b>25114</b> includes traffic-affecting information.

0194In embodiments, the social media data <b>25114</b> includes an indication of a specific individual at a location. In embodiments, the social media data <b>25114</b> includes an indication of a celebrity at a location. In embodiments, the social media data <b>25114</b> includes presence of a rare or transient phenomena at a location. In embodiments, the social media data <b>25114</b> includes a commerce-related event. In embodiments, the social media data <b>25114</b> includes an entertainment event at a location. In embodiments, the social media data <b>25114</b> includes traffic conditions. In embodiments, the social media data <b>25114</b> includes weather conditions. In embodiments, the social media data <b>25114</b> includes entertainment options.

0195In embodiments, the social media data <b>25114</b> includes risk-related conditions. In embodiments, the social media data <b>25114</b> includes predictions of attendance at an event. In embodiments, the social media data <b>25114</b> includes estimates of attendance at an event. In embodiments, the social media data <b>25114</b> includes modes of transportation used with an event. In embodiments, the effect on the transportation system includes reducing fuel consumption. In embodiments, the effect on the transportation system includes reducing traffic congestion. In embodiments, the effect on the transportation system includes reduced carbon footprint. In embodiments, the effect on the transportation system includes reduced pollution. In embodiments, the optimized state of the vehicle is an operating state of the vehicle.

0196In embodiments, the optimized state of the vehicle includes an in-vehicle state. In embodiments, the optimized state of the vehicle includes a rider state. In embodiments, the optimized state of the vehicle includes a routing state. In embodiments, the optimized state of the vehicle includes user experience state. In embodiments, a characterization of an outcome of the optimizing in the social media data is used as feedback to improve the optimizing. In embodiments, the feedback includes likes or dislikes of the outcome. In embodiments, the feedback includes social medial activity referencing the outcome. In embodiments, the feedback includes trending of social media activity referencing the outcome.

0197In embodiments, the feedback includes hash tags associated with the outcome. In embodiments, the feedback includes ratings of the outcome. In embodiments, the feedback includes requests for the outcome.

0198Referring to <figref idref="DRAWINGS">FIG. <b>27</b></figref>, in embodiments provided herein are transportation systems <b>2711</b> having a data processing system <b>2762</b> for taking data <b>27114</b> from a plurality <b>2769</b> of social data sources <b>27107</b> and using a hybrid neural network <b>2747</b> to optimize satisfaction <b>27121</b> of at least one rider <b>27120</b> in a vehicle <b>2710</b> based on processing the social data sources with the hybrid neural network <b>2747</b>. Social data sources <b>2769</b> may be used, for example, to predict what entertainment options are most likely to be effective for a rider <b>27120</b> by one neural network category, while another neural network category may be used to optimize a routing plan (such as based on social data that indicates likely traffic, points of interest, or the like). Social data <b>27114</b> may also be used for outcome tracking and feedback to optimize the system, both as to entertainment options and as to transportation planning, routing, or the like.

0199An aspect provided herein includes a system for transportation <b>2711</b>, comprising: a data processing system <b>2762</b> for taking data <b>27114</b> from a plurality <b>2769</b> of social data sources <b>27107</b> and using a hybrid neural network <b>2747</b> to optimize satisfaction <b>27121</b> of at least one rider <b>27120</b> in a vehicle <b>2710</b> based on processing the data <b>27114</b> from the plurality <b>2769</b> of social data sources <b>27107</b> with the hybrid neural network <b>2747</b>.

0200<figref idref="DRAWINGS">FIG. <b>28</b></figref> illustrates a method <b>2800</b> of optimizing rider satisfaction in accordance with embodiments of the systems and methods disclosed herein. At <b>2802</b> the method includes classifying, using a first neural network <b>2722</b> (<figref idref="DRAWINGS">FIG. <b>27</b></figref>) of a hybrid neural network, social media data <b>27119</b> (<figref idref="DRAWINGS">FIG. <b>27</b></figref>) sourced from a plurality of social media sources as indicative of an effect on a transportation system. At <b>2804</b> the method includes predicting, using a second neural network <b>2720</b> (<figref idref="DRAWINGS">FIG. <b>27</b></figref>) of the hybrid neural network, at least one aspect <b>27122</b> (<figref idref="DRAWINGS">FIG. <b>27</b></figref>) of rider satisfaction affected by an effect on the transportation system derived from the social media data classified as indicative of an effect on the transportation system. At <b>2806</b> the method includes optimizing, using a third neural network <b>27117</b> (<figref idref="DRAWINGS">FIG. <b>27</b></figref>) of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying a vehicle in the transportation system.

0201Referring to <figref idref="DRAWINGS">FIG. <b>27</b></figref> and <figref idref="DRAWINGS">FIG. <b>28</b></figref>, in embodiments, at least one of the neural networks in the hybrid neural network <b>2547</b> is a convolutional neural network. In embodiments, the at least one aspect of rider satisfaction <b>27121</b> is optimized by predicting an entertainment option for presenting to the rider. In embodiments, the at least one aspect of rider satisfaction <b>27121</b> is optimized by optimizing route planning for a vehicle occupied by the rider. In embodiments, the at least one aspect of rider satisfaction <b>27121</b> is a rider state and optimizing the aspects of rider satisfaction comprising optimizing the rider state. In embodiments, social media data specific to the rider is analyzed to determine at least one optimizing action likely to optimize the at least one aspect of rider satisfaction <b>27121</b>. In embodiments, the optimizing action is selected from the group of actions consisting of adjusting a routing plan to include passing points of interest to the user, avoiding traffic congestion predicted from the social media data, and presenting entertainment options.

0202In embodiments, the social media data includes social media posts. In embodiments, the social media data includes social media feeds. In embodiments, the social media data includes like or dislike activity detected in the social media. In embodiments, the social media data includes indications of relationships. In embodiments, the social media data includes user behavior. In embodiments, the social media data includes discussion threads. In embodiments, the social media data includes chats. In embodiments, the social media data includes photographs.

0203In embodiments, the social media data includes traffic-affecting information. In embodiments, the social media data includes an indication of a specific individual at a location. In embodiments, the social media data includes an indication of a celebrity at a location. In embodiments, the social media data includes presence of a rare or transient phenomena at a location. In embodiments, the social media data includes a commerce-related event. In embodiments, the social media data includes an entertainment event at a location. In embodiments, the social media data includes traffic conditions. In embodiments, the social media data includes weather conditions. In embodiments, the social media data includes entertainment options. In embodiments, the social media data includes risk-related conditions. In embodiments, the social media data includes predictions of attendance at an event. In embodiments, the social media data includes estimates of attendance at an event. In embodiments, the social media data includes modes of transportation used with an event. In embodiments, the effect on the transportation system includes reducing fuel consumption. In embodiments, the effect on the transportation system includes reducing traffic congestion. In embodiments, the effect on the transportation system includes reduced carbon footprint. In embodiments, the effect on the transportation system includes reduced pollution. In embodiments, the optimized at least one aspect of rider satisfaction is an operating state of the vehicle. In embodiments, the optimized at least one aspect of rider satisfaction includes an in-vehicle state. In embodiments, the optimized at least one aspect of rider satisfaction includes a rider state. In embodiments, the optimized at least one aspect of rider satisfaction includes a routing state. In embodiments, the optimized at least one aspect of rider satisfaction includes user experience state.

0204In embodiments, a characterization of an outcome of the optimizing in the social media data is used as feedback to improve the optimizing. In embodiments, the feedback includes likes or dislikes of the outcome. In embodiments, the feedback includes social medial activity referencing the outcome. In embodiments, the feedback includes trending of social media activity referencing the outcome. In embodiments, the feedback includes hash tags associated with the outcome. In embodiments, the feedback includes ratings of the outcome. In embodiments, the feedback includes requests for the outcome.

0205An aspect provided herein includes a rider satisfaction system <b>27123</b> for optimizing rider satisfaction <b>27121</b>, the system comprising: a first neural network <b>2722</b> of a hybrid neural network <b>2747</b> to classify social media data <b>27114</b> sourced from a plurality <b>2769</b> of social media sources <b>27107</b> as indicative of an effect <b>27119</b> on a transportation system <b>2711</b>; a second neural network <b>2720</b> of the hybrid neural network <b>2747</b> to predict at least one aspect <b>27122</b> of rider satisfaction <b>27121</b> affected by an effect on the transportation system derived from the social media data classified as indicative of the effect on the transportation system; and a third network <b>27117</b> of the hybrid neural network <b>2747</b> to optimize the at least one aspect of rider satisfaction <b>27121</b> for at least one rider <b>2744</b> occupying a vehicle <b>2710</b> in the transportation system <b>2711</b>. In embodiments, at least one of the neural networks in the hybrid neural network <b>2747</b> is a convolutional neural network.

0206In embodiments, the at least one aspect of rider satisfaction <b>27121</b> is optimized by predicting an entertainment option for presenting to the rider <b>2744</b>. In embodiments, the at least one aspect of rider satisfaction <b>27121</b> is optimized by optimizing route planning for a vehicle <b>2710</b> occupied by the rider <b>2744</b>. In embodiments, the at least one aspect of rider satisfaction <b>27121</b> is a rider state <b>2737</b> and optimizing the at least one aspect of rider satisfaction <b>27121</b> comprises optimizing the rider state <b>2737</b>. In embodiments, social media data specific to the rider <b>2744</b> is analyzed to determine at least one optimizing action likely to optimize the at least one aspect of rider satisfaction <b>27121</b>. In embodiments, the at least one optimizing action is selected from the group consisting of: adjusting a routing plan to include passing points of interest to the user, avoiding traffic congestion predicted from the social media data, deriving an economic benefit, deriving an altruistic benefit, and presenting entertainment options.

0207In embodiments, the economic benefit is saved fuel. In embodiments, the altruistic benefit is reduction of environmental impact. In embodiments, the social media data includes social media posts. In embodiments, the social media data includes social media feeds. In embodiments, the social media data includes like or dislike activity detected in the social media. In embodiments, the social media data includes indications of relationships. In embodiments, the social media data includes user behavior. In embodiments, the social media data includes discussion threads. In embodiments, the social media data includes chats. In embodiments, the social media data includes photographs. In embodiments, the social media data includes traffic-affecting information. In embodiments, the social media data includes an indication of a specific individual at a location.

0208In embodiments, the social media data includes an indication of a celebrity at a location. In embodiments, the social media data includes presence of a rare or transient phenomena at a location. In embodiments, the social media data includes a commerce-related event. In embodiments, the social media data includes an entertainment event at a location. In embodiments, the social media data includes traffic conditions. In embodiments, the social media data includes weather conditions. In embodiments, the social media data includes entertainment options. In embodiments, the social media data includes risk-related conditions. In embodiments, the social media data includes predictions of attendance at an event. In embodiments, the social media data includes estimates of attendance at an event. In embodiments, the social media data includes modes of transportation used with an event.

0209In embodiments, the effect on the transportation system includes reducing fuel consumption. In embodiments, the effect on the transportation system includes reducing traffic congestion. In embodiments, the effect on the transportation system includes reduced carbon footprint. In embodiments, the effect on the transportation system includes reduced pollution. In embodiments, the optimized at least one aspect of rider satisfaction is an operating state of the vehicle. In embodiments, the optimized at least one aspect of rider satisfaction includes an in-vehicle state. In embodiments, the optimized at least one aspect of rider satisfaction includes a rider state. In embodiments, the optimized at least one aspect of rider satisfaction includes a routing state. In embodiments, the optimized at least one aspect of rider satisfaction includes user experience state. In embodiments, a characterization of an outcome of the optimizing in the social media data is used as feedback to improve the optimizing. In embodiments, the feedback includes likes or dislikes of the outcome. In embodiments, the feedback includes social medial activity referencing the outcome. In embodiments, the feedback includes trending of social media activity referencing the outcome. In embodiments, the feedback includes hash tags associated with the outcome. In embodiments, the feedback includes ratings of the outcome. In embodiments, the feedback includes requests for the outcome.

0210Referring to <figref idref="DRAWINGS">FIG. <b>29</b></figref>, in embodiments provided herein are transportation systems <b>2911</b> having a hybrid neural network <b>2947</b> wherein one neural network <b>2922</b> processes a sensor input <b>29125</b> about a rider <b>2944</b> of a vehicle <b>2910</b> to determine an emotional state <b>29126</b> and another neural network optimizes at least one operating parameter <b>29124</b> of the vehicle to improve the rider's emotional state <b>2966</b>. For example, a neural net <b>2922</b> that includes one or more perceptrons <b>29127</b> that mimic human senses may be used to mimic or assist with determining the likely emotional state of a rider <b>29126</b> based on the extent to which various senses have been stimulated, while another neural network <b>2920</b> is used in an expert system that performs random and/or systematized variations of various combinations of operating parameters (such as entertainment settings, seat settings, suspension settings, route types and the like) with genetic programming that promotes favorable combinations and eliminates unfavorable ones, optionally based on input from the output of the perceptron-containing neural network <b>2922</b> that predict emotional state. These and many other such combinations are encompassed by the present disclosure. In <figref idref="DRAWINGS">FIG. <b>29</b></figref>, perceptrons <b>29127</b> are depicted as optional.

0211An aspect provided herein includes a system for transportation <b>2911</b>, comprising: a hybrid neural network <b>2947</b> wherein one neural network <b>2922</b> processes a sensor input <b>29125</b> corresponding to a rider <b>2944</b> of a vehicle <b>2910</b> to determine an emotional state <b>2966</b> of the rider <b>2944</b> and another neural network <b>2920</b> optimizes at least one operating parameter <b>29124</b> of the vehicle to improve the emotional state <b>2966</b> of the rider <b>2944</b>.

0212An aspect provided herein includes a hybrid neural network <b>2947</b> for rider satisfaction, comprising: a first neural network <b>2922</b> to detect a detected emotional state <b>29126</b> of a rider <b>2944</b> occupying a vehicle <b>2910</b> through analysis of data <b>29125</b> gathered from sensors <b>2925</b> deployed in a vehicle <b>2910</b> for gathering physiological conditions of the rider; and a second neural network <b>2920</b> to optimize, for achieving a favorable emotional state of the rider, an operational parameter <b>29124</b> of the vehicle in response to the detected emotional state <b>29126</b> of the rider.

0213In embodiments, the first neural network <b>2922</b> is a recurrent neural network and the second neural network <b>2920</b> is a radial basis function neural network. In embodiments, at least one of the neural networks in the hybrid neural network <b>2947</b> is a convolutional neural network. In embodiments, the second neural network <b>2920</b> is to optimize the operational parameter <b>29124</b> based on a correlation between a vehicle operating state <b>2945</b> and a rider emotional state <b>2966</b> of the rider. In embodiments, the second neural network <b>2920</b> optimizes the operational parameter <b>29124</b> in real time responsive to the detecting of the detected emotional state <b>29126</b> of the rider <b>2944</b> by the first neural network <b>2922</b>. In embodiments, the first neural network <b>2922</b> comprises a plurality of connected nodes that form a directed cycle, the first neural network <b>2922</b> further facilitating bi-directional flow of data among the connected nodes. In embodiments, the operational parameter <b>29124</b> that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.

0214An aspect provided herein includes an artificial intelligence system <b>2936</b> for optimizing rider satisfaction, comprising: a hybrid neural network <b>2947</b>, including: a recurrent neural network (e.g., in <figref idref="DRAWINGS">FIG. <b>29</b></figref>, neural network <b>2922</b> may be a recurrent neural network) to indicate a change in an emotional state of a rider <b>2944</b> in a vehicle <b>2910</b> through recognition of patterns of physiological data of the rider captured by at least one sensor <b>2925</b> deployed for capturing rider emotional state-indicative data while occupying the vehicle <b>2910</b>; and a radial basis function neural network (e.g., in <figref idref="DRAWINGS">FIG. <b>29</b></figref>, neural network <b>2920</b> may be a radial basis function neural network) to optimize, for achieving a favorable emotional state of the rider, an operational parameter <b>29124</b> of the vehicle in response to the indication of change in the emotional state of the rider. In embodiments, the operational parameter <b>29124</b> of the vehicle that is to be optimized is to be determined and adjusted to induce the favorable emotional state of the rider.

0215An aspect provided herein includes an artificial intelligence system <b>2936</b> for optimizing rider satisfaction, comprising: a hybrid neural network <b>2947</b>, including: a convolutional neural network (in <figref idref="DRAWINGS">FIG. <b>29</b></figref>, neural network <b>1</b>, depicted at reference numeral <b>2922</b>, may optionally be a convolutional neural network) to indicate a change in an emotional state of a rider in a vehicle through recognitions of patterns of visual data of the rider captured by at least one image sensor (in <figref idref="DRAWINGS">FIG. <b>29</b></figref>, the sensor <b>2925</b> may optionally be an image sensor) deployed for capturing images of the rider while occupying the vehicle; and a second neural network <b>2920</b> to optimize, for achieving a favorable emotional state of the rider, an operational parameter <b>29124</b> of the vehicle in response to the indication of change in the emotional state of the rider.

0216In embodiments, the operational parameter <b>19124</b> of the vehicle that is to be optimized is to be determined and adjusted to induce the favorable emotional state of the rider.

0217Referring to <figref idref="DRAWINGS">FIG. <b>30</b></figref>, in embodiments provided herein are transportation systems <b>3011</b> having an artificial intelligence system <b>3036</b> for processing feature vectors of an image of a face of a rider in a vehicle to determine an emotional state and optimizing at least one operating parameter of the vehicle to improve the rider's emotional state. A face may be classified based on images from in-vehicle cameras, available cellphone or other mobile device cameras, or other sources. An expert system, optionally trained based on a training set of data provided by humans or trained by deep learning, may learn to adjust vehicle parameters (such as any described herein) to provide improved emotional states. For example, if a rider's face indicates stress, the vehicle may select a less stressful route, play relaxing music, play humorous content, or the like.

0218An aspect provided herein includes a transportation system <b>3011</b>, comprising: an artificial intelligence system <b>3036</b> for processing feature vectors <b>30130</b> of an image <b>30129</b> of a face <b>30128</b> of a rider <b>3044</b> in a vehicle <b>3010</b> to determine an emotional state <b>3066</b> of the rider and optimizing an operational parameter <b>30124</b> of the vehicle to improve the emotional state <b>3066</b> of the rider <b>3044</b>.

0219In embodiments, the artificial intelligence system <b>3036</b> includes: a first neural network <b>3022</b> to detect the emotional state <b>30126</b> of the rider through recognition of patterns of the feature vectors <b>30130</b> of the image <b>30129</b> of the face <b>30128</b> of the rider <b>3044</b> in the vehicle <b>3010</b>, the feature vectors <b>30130</b> indicating at least one of a favorable emotional state of the rider and an unfavorable emotional state of the rider; and a second neural network <b>3020</b> to optimize, for achieving the favorable emotional state of the rider, the operational parameter <b>30124</b> of the vehicle in response to the detected emotional state <b>30126</b> of the rider.

0220In embodiments, the first neural network <b>3022</b> is a recurrent neural network and the second neural network <b>3020</b> is a radial basis function neural network. In embodiments, the second neural network <b>3020</b> optimizes the operational parameter <b>30124</b> based on a correlation between the vehicle operating state <b>3045</b> and the emotional state <b>3066</b> of the rider. In embodiments, the second neural network <b>3020</b> is to determine an optimum value for the operational parameter of the vehicle, and the transportation system <b>3011</b> is to adjust the operational parameter <b>30124</b> of the vehicle to the optimum value to induce the favorable emotional state of the rider. In embodiments, the first neural network <b>3022</b> further learns to classify the patterns in the feature vectors and associate the patterns with a set of emotional states and changes thereto by processing a training data set <b>30131</b>. In embodiments, the training data set <b>30131</b> is sourced from at least one of a stream of data from an unstructured data source, a social media source, a wearable device, an in-vehicle sensor, a rider helmet, a rider headgear, and a rider voice recognition system.

0221In embodiments, the second neural network <b>3020</b> optimizes the operational parameter <b>30124</b> in real time responsive to the detecting of the emotional state of the rider by the first neural network <b>3022</b>. In embodiments, the first neural network <b>3022</b> is to detect a pattern of the feature vectors. In embodiments, the pattern is associated with a change in the emotional state of the rider from a first emotional state to a second emotional state. In embodiments, the second neural network <b>3020</b> optimizes the operational parameter of the vehicle in response to the detection of the pattern associated with the change in the emotional state. In embodiments, the first neural network <b>3022</b> comprises a plurality of interconnected nodes that form a directed cycle, the first neural network <b>3022</b> further facilitating bi-directional flow of data among the interconnected nodes. In embodiments, the transportation system <b>3011</b> further comprises: a feature vector generation system to process a set of images of the face of the rider, the set of images captured over an interval of time from by a plurality of image capture devices <b>3027</b> while the rider <b>3044</b> is in the vehicle <b>3010</b>, wherein the processing of the set of images is to produce the feature vectors <b>30130</b> of the image of the face of the rider. In embodiments, the transportation system further comprises: image capture devices <b>3027</b> disposed to capture a set of images of the face of the rider in the vehicle from a plurality of perspectives; and an image processing system to produce the feature vectors from the set of images captured from at least one of the plurality of perspectives.

0222In embodiments, the transportation system <b>3011</b> further comprises an interface <b>30133</b> between the first neural network and the image processing system <b>30132</b> to communicate a time sequence of the feature vectors, wherein the feature vectors are indicative of the emotional state of the rider. In embodiments, the feature vectors indicate at least one of a changing emotional state of the rider, a stable emotional state of the rider, a rate of change of the emotional state of the rider, a direction of change of the emotional state of the rider, a polarity of a change of the emotional state of the rider; the emotional state of the rider is changing to the unfavorable emotional state; and the emotional state of the rider is changing to the favorable emotional state.

0223In embodiments, the operational parameter that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the second neural network is to interact with a vehicle control system to adjust the operational parameter. In embodiments, the artificial intelligence system further comprises a neural network that includes one or more perceptrons that mimic human senses that facilitates determining the emotional state of the rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the artificial intelligence system includes: a recurrent neural network to indicate a change in the emotional state of the rider through recognition of patterns of the feature vectors of the image of the face of the rider in the vehicle; and a radial basis function neural network to optimize, for achieving the favorable emotional state of the rider, the operational parameter of the vehicle in response to the indication of the change in the emotional state of the rider.

0224In embodiments, the radial basis function neural network is to optimize the operational parameter based on a correlation between a vehicle operating state and a rider emotional state. In embodiments, the operational parameter of the vehicle that is optimized is determined and adjusted to induce a favorable rider emotional state. In embodiments, the recurrent neural network further learns to classify the patterns of the feature vectors and associate the patterns of the feature vectors to emotional states and changes thereto from a training data set sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. In embodiments, the radial basis function neural network is to optimize the operational parameter in real time responsive to the detecting of the change in the emotional state of the rider by the recurrent neural network. In embodiments, the recurrent neural network detects a pattern of the feature vectors that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the radial basis function neural network is to optimize the operational parameter of the vehicle in response to the indicated change in emotional state.

0225In embodiments, the recurrent neural network comprises a plurality of connected nodes that form a directed cycle, the recurrent neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the feature vectors indicate at least one of the emotional state of the rider is changing, the emotional state of the rider is stable, a rate of change of the emotional state of the rider, a direction of change of the emotional state of the rider, and a polarity of a change of the emotional state of the rider; the emotional state of a rider is changing to an unfavorable emotional state; and an emotional state of a rider is changing to a favorable emotional state. In embodiments, the operational parameter that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route.

0226In embodiments, the radial basis function neural network is to interact with a vehicle control system <b>30134</b> to adjust the operational parameter <b>30124</b>. In embodiments, the artificial intelligence system <b>3036</b> further comprises a neural network that includes one or more perceptrons that mimic human senses that facilitates determining the emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the artificial intelligence system <b>3036</b> is to maintain the favorable emotional state of the rider via a modular neural network, the modular neural network comprising: a rider emotional state determining neural network to process the feature vectors of the image of the face of the rider in the vehicle to detect patterns. In embodiments, the patterns in the feature vectors indicate at least one of the favorable emotional state and the unfavorable emotional state; an intermediary circuit to convert data from the rider emotional state determining neural network into vehicle operational state data; and a vehicle operational state optimizing neural network to adjust an operational parameter of the vehicle in response to the vehicle operational state data.

0227In embodiments, the vehicle operational state optimizing neural network is to adjust the operational parameter <b>30124</b> of the vehicle for achieving a favorable emotional state of the rider. In embodiments, the vehicle operational state optimizing neural network is to optimize the operational parameter based on a correlation between a vehicle operating state <b>3045</b> and a rider emotional state <b>3066</b>. In embodiments, the operational parameter of the vehicle that is optimized is determined and adjusted to induce a favorable rider emotional state. In embodiments, the rider emotional state determining neural network further learns to classify the patterns of the feature vectors and associate the pattern of the feature vectors to emotional states and changes thereto from a training data set sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system.

0228In embodiments, the vehicle operational state optimizing neural network is to optimize the operational parameter <b>30124</b> in real time responsive to the detecting of a change in an emotional state <b>30126</b> of the rider by the rider emotional state determining neural network. In embodiments, the rider emotional state determining neural network is to detect a pattern of the feature vectors <b>30130</b> that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the vehicle operational state optimizing neural network is to optimize the operational parameter of the vehicle in response to the indicated change in emotional state. In embodiments, the artificial intelligence system <b>3036</b> comprises a plurality of connected nodes that form a directed cycle, the artificial intelligence system further facilitating bi-directional flow of data among the connected nodes.

0229In embodiments, the feature vectors <b>30130</b> indicate at least one of the emotional state of the rider is changing, the emotional state of the rider is stable, a rate of change of the emotional state of the rider, a direction of change of the emotional state of the rider, and a polarity of a change of the emotional state of the rider; the emotional state of a rider is changing to an unfavorable emotional state; and the emotional state of the rider is changing to a favorable emotional state. In embodiments, the operational parameter that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the vehicle operational state optimizing neural network interacts with a vehicle control system to adjust the operational parameter.

0230In embodiments, the artificial intelligence system <b>3036</b> further comprises a neural net that includes one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. It is to be understood that the terms “neural net” and “neural network” are used interchangeably in the present disclosure. In embodiments, the rider emotional state determining neural network comprises one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the artificial intelligence system <b>3036</b> includes a recurrent neural network to indicate a change in the emotional state of the rider in the vehicle through recognition of patterns of the feature vectors of the image of the face of the rider in the vehicle; the transportation system further comprising: a vehicle control system <b>30134</b> to control operation of the vehicle by adjusting a plurality of vehicle operational parameters <b>30124</b>; and a feedback loop to communicate the indicated change in the emotional state of the rider between the vehicle control system <b>30134</b> and the artificial intelligence system <b>3036</b>. In embodiments, the vehicle control system is to adjust at least one of the plurality of vehicle operational parameters <b>30124</b> in response to the indicated change in the emotional state of the rider. In embodiments, the vehicle controls system adjusts the at least one of the plurality of vehicle operational parameters based on a correlation between vehicle operational state and rider emotional state.

0231In embodiments, the vehicle control system adjusts the at least one of the plurality of vehicle operational parameters <b>30124</b> that are indicative of a favorable rider emotional state. In embodiments, the vehicle control system <b>30134</b> selects an adjustment of the at least one of the plurality of vehicle operational parameters <b>30124</b> that is indicative of producing a favorable rider emotional state. In embodiments, the recurrent neural network further learns to classify the patterns of feature vectors and associate them to emotional states and changes thereto from a training data set <b>30131</b> sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. In embodiments, the vehicle control system <b>30134</b> adjusts the at least one of the plurality of vehicle operation parameters <b>30124</b> in real time. In embodiments, the recurrent neural network detects a pattern of the feature vectors that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the vehicle operation control system adjusts an operational parameter of the vehicle in response to the indicated change in emotional state. In embodiments, the recurrent neural network comprises a plurality of connected nodes that form a directed cycle, the recurrent neural network further facilitating bi-directional flow of data among the connected nodes.

0232In embodiments, the feature vectors indicating at least one of an emotional state of the rider is changing, an emotional state of the rider is stable, a rate of change of an emotional state of the rider, a direction of change of an emotional state of the rider, and a polarity of a change of an emotional state of the rider; an emotional state of a rider is changing to an unfavorable state; an emotional state of a rider is changing to a favorable state. In embodiments, the at least one of the plurality of vehicle operational parameters responsively adjusted affects a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, proximity to other vehicles along the route. In embodiments, the at least one of the plurality of vehicle operation parameters that is responsively adjusted affects operation of a powertrain of the vehicle and a suspension system of the vehicle. In embodiments, the radial basis function neural network interacts with the recurrent neural network via an intermediary component of the artificial intelligence system <b>3036</b> that produces vehicle control data indicative of an emotional state response of the rider to a current operational state of the vehicle. In embodiments, the recognition of patterns of feature vectors comprises processing the feature vectors of the image of the face of the rider captured during at least two of before the adjusting at least one of the plurality of vehicle operational parameters, during the adjusting at least one of the plurality of vehicle operational parameters, and after adjusting at least one of the plurality of vehicle operational parameters.

0233In embodiments, the adjusting at least one of the plurality of vehicle operational parameters <b>30124</b> improves an emotional state of a rider in a vehicle. In embodiments, the adjusting at least one of the plurality of vehicle operational parameters causes an emotional state of the rider to change from an unfavorable emotional state to a favorable emotional state. In embodiments, the change is indicated by the recurrent neural network. In embodiments, the recurrent neural network indicates a change in the emotional state of the rider responsive to a change in an operating parameter of the vehicle by determining a difference between a first set of feature vectors of an image of the face of a rider captured prior to the adjusting at least one of the plurality of operating parameters and a second set of feature vectors of an image of the face of the rider captured during or after the adjusting at least one of the plurality of operating parameters.

0234In embodiments, the recurrent neural network detects a pattern of the feature vectors that indicates an emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the vehicle operation control system adjusts an operational parameter of the vehicle in response to the indicated change in emotional state.

0235Referring to <figref idref="DRAWINGS">FIG. <b>31</b></figref>, in embodiments, provided herein are transportation systems having an artificial intelligence system for processing a voice of a rider in a vehicle to determine an emotional state and optimizing at least one operating parameter of the vehicle to improve the rider's emotional state. A voice-analysis module may take voice input and, using a training set of labeled data where individuals indicate emotional states while speaking and/or whether others tag the data to indicate perceived emotional states while individuals are talking, a machine learning system (such as any of the types described herein) may be trained (such as using supervised learning, deep learning, or the like) to classify the emotional state of the individual based on the voice. Machine learning may improve classification by using feedback from a large set of trials, where feedback in each instance indicates whether the system has correctly assessed the emotional state of the individual in the case of an instance of speaking. Once trained to classify the emotional state, an expert system (optionally using a different machine learning system or other artificial intelligence system) may, based on feedback of outcomes of the emotional states of a set of individuals, be trained to optimize various vehicle parameters noted throughout this disclosure to maintain or induce more favorable states. For example, among many other indicators, where a voice of an individual indicates happiness, the expert system may select or recommend upbeat music to maintain that state. Where a voice indicates stress, the system may recommend or provide a control signal to change a planned route to one that is less stressful (e.g., has less stop-and-go traffic, or that has a higher probability of an on-time arrival). In embodiments, the system may be configured to engage in a dialog (such as on on-screen dialog or an audio dialog), such as using an intelligent agent module of the system, that is configured to use a series of questions to help obtain feedback from a user about the user's emotional state, such as asking the rider about whether the rider is experiencing stress, what the source of the stress may be (e.g., traffic conditions, potential for late arrival, behavior of other drivers, or other sources unrelated to the nature of the ride), what might mitigate the stress (route options, communication options (such as offering to send a note that arrival may be delayed), entertainment options, ride configuration options, and the like), and the like. Driver responses may be fed as inputs to the expert system as indicators of emotional state, as well as to constrain efforts to optimize one or more vehicle parameters, such as by eliminating options for configuration that are not related to a driver's source of stress from a set of available configurations.

0236An aspect provided herein includes a system for transportation <b>3111</b>, comprising: an artificial intelligence system <b>3136</b> for processing a voice <b>31135</b> of a rider <b>3144</b> in a vehicle <b>3110</b> to determine an emotional state <b>3166</b> of the rider <b>3144</b> and optimizing at least one operating parameter <b>31124</b> of the vehicle <b>3110</b> to improve the emotional state <b>3166</b> of the rider <b>3144</b>.

0237An aspect provided herein includes an artificial intelligence system <b>3136</b> for voice processing to improve rider satisfaction in a transportation system <b>3111</b>, comprising: a rider voice capture system <b>30136</b> deployed to capture voice output <b>31128</b> of a rider <b>3144</b> occupying a vehicle <b>3110</b>; a voice-analysis circuit <b>31132</b> trained using machine learning that classifies an emotional state <b>31138</b> of the rider for the captured voice output of the rider; and an expert system <b>31139</b> trained using machine learning that optimizes at least one operating parameter <b>31124</b> of the vehicle to change the rider emotional state to an emotional state classified as an improved emotional state.

0238In embodiments, the rider voice capture system <b>31136</b> comprises an intelligent agent <b>31140</b> that engages in a dialog with the rider to obtain rider feedback for use by the voice-analysis circuit <b>31132</b> for rider emotional state classification. In embodiments, the voice-analysis circuit <b>31132</b> uses a first machine learning system and the expert system <b>31139</b> uses a second machine learning system. In embodiments, the expert system <b>31139</b> is trained to optimize the at least one operating parameter <b>31124</b> based on feedback of outcomes of the emotional states when adjusting the at least one operating parameter <b>31124</b> for a set of individuals. In embodiments, the emotional state <b>3166</b> of the rider is determined by a combination of the captured voice output <b>31128</b> of the rider and at least one other parameter. In embodiments, the at least one other parameter is a camera-based emotional state determination of the rider. In embodiments, the at least one other parameter is traffic information. In embodiments, the at least one other parameter is weather information. In embodiments, the at least one other parameter is a vehicle state. In embodiments, the at least one other parameter is at least one pattern of physiological data of the rider. In embodiments, the at least one other parameter is a route of the vehicle. In embodiments, the at least one other parameter is in-vehicle audio content. In embodiments, the at least one other parameter is a speed of the vehicle. In embodiments, the at least one other parameter is acceleration of the vehicle. In embodiments, the at least one other parameter is deceleration of the vehicle. In embodiments, the at least one other parameter is proximity to objects along the route. In embodiments, the at least one other parameter is proximity to other vehicles along the route.

0239An aspect provided herein includes an artificial intelligence system <b>3136</b> for voice processing to improve rider satisfaction, comprising: a first neural network <b>3122</b> trained to classify emotional states based on analysis of human voices detects an emotional state of a rider through recognition of aspects of the voice <b>31128</b> of the rider captured while the rider is occupying the vehicle <b>3110</b> that correlate to at least one emotional state <b>3166</b> of the rider; and a second neural network <b>3120</b> that optimizes, for achieving a favorable emotional state of the rider, an operational parameter <b>31124</b> of the vehicle in response to the detected emotional state <b>31126</b> of the rider <b>3144</b>. In embodiments, at least one of the neural networks is a convolutional neural network. In embodiments, the first neural network <b>3122</b> is trained through use of a training data set that associates emotional state classes with human voice patterns. In embodiments, the first neural network <b>3122</b> is trained through the use of a training data set of voice recordings that are tagged with emotional state identifying data. In embodiments, the emotional state of the rider is determined by a combination of the captured voice output of the rider and at least one other parameter. In embodiments, the at least one other parameter is a camera-based emotional state determination of the rider. In embodiments, the at least one other parameter is traffic information. In embodiments, the at least one other parameter is weather information. In embodiments, the at least one other parameter is a vehicle state.

0240In embodiments, the at least one other parameter is at least one pattern of physiological data of the rider. In embodiments, the at least one other parameter is a route of the vehicle. In embodiments, the at least one other parameter is in-vehicle audio content. In embodiments, the at least one other parameter is a speed of the vehicle. In embodiments, the at least one other parameter is acceleration of the vehicle. In embodiments, the at least one other parameter is deceleration of the vehicle. In embodiments, the at least one other parameter is proximity to objects along the route. In embodiments, the at least one other parameter is proximity to other vehicles along the route.

0241Referring now to <figref idref="DRAWINGS">FIG. <b>32</b></figref>, in embodiments provided herein are transportation systems <b>3211</b> having an artificial intelligence system <b>3236</b> for processing data from an interaction of a rider with an electronic commerce system of a vehicle to determine a rider state and optimizing at least one operating parameter of the vehicle to improve the rider's state. Another common activity for users of device interfaces is e-commerce, such as shopping, bidding in auctions, selling items and the like. E-commerce systems use search functions, undertake advertising and engage users with various work flows that may eventually result in an order, a purchase, a bid, or the like. As described herein with search, a set of in-vehicle-relevant search results may be provided for e-commerce, as well as in-vehicle relevant advertising. In addition, in-vehicle-relevant interfaces and workflows may be configured based on detection of an in-vehicle rider, which may be quite different than workflows that are provided for e-commerce interfaces that are configured for smart phones or for desktop systems. Among other factors, an in-vehicle system may have access to information that is unavailable to conventional e-commerce systems, including route information (including direction, planned stops, planned duration and the like), rider mood and behavior information (such as from past routes, as well as detected from in-vehicle sensor sets), vehicle configuration and state information (such as make and model), and any of the other vehicle-related parameters described throughout this disclosure. As one example, a rider who is bored (as detected by an in-vehicle sensor set, such as using an expert system that is trained to detect boredom) and is on a long trip (as indicated by a route that is being undertaken by a car) may be far more patient, and likely to engage in deeper, richer content, and longer workflows, than a typical mobile user. As another example, an in-vehicle rider may be far more likely to engage in free trials, surveys, or other behaviors that promote brand engagement. Also, an in-vehicle user may be motivated to use otherwise down time to accomplish specific goals, such as shopping for needed items. Presenting the same interfaces, content, and workflows to in-vehicle users may miss excellent opportunities for deeper engagement that would be highly unlikely in other settings where many more things may compete for a user's attention. In embodiments, an e-commerce system interface may be provided for in-vehicle users, where at least one of interface displays, content, search results, advertising, and one or more associated workflows (such as for shopping, bidding, searching, purchasing, providing feedback, viewing products, entering ratings or reviews, or the like) is configured based on the detection of the use of an in-vehicle interface. Displays and interactions may be further configured (optionally based on a set of rules or based on machine learning), such as based on detection of display types (e.g., allowing richer or larger images for large, HD displays), network capabilities (e.g., enabling faster loading and lower latency by caching low-resolution images that initially render), audio system capabilities (such as using audio for dialog management and intelligence assistant interactions) and the like for the vehicle. Display elements, content, and workflows may be configured by machine learning, such as by A/B testing and/or using genetic programming techniques, such as configuring alternative interaction types and tracking outcomes. Outcomes used to train automatic configuration of workflows for in-vehicle e-commerce interfaces may include extent of engagement, yield, purchases, rider satisfaction, ratings, and others. In-vehicle users may be profiled and clustered, such as by behavioral profiling, demographic profiling, psychographic profiling, location-based profiling, collaborative filtering, similarity-based clustering, or the like, as with conventional e-commerce, but profiles may be enhanced with route information, vehicle information, vehicle configuration information, vehicle state information, rider information and the like. A set of in-vehicle user profiles, groups and clusters may be maintained separately from conventional user profiles, such that learning on what content to present, and how to present it, is accomplished with increased likelihood that the differences in in-vehicle shopping area accounted for when targeting search results, advertisements, product offers, discounts, and the like.

0242An aspect provided herein includes a system for transportation <b>3211</b>, comprising: an artificial intelligence system <b>3236</b> for processing data from an interaction of a rider <b>3244</b> with an electronic commerce system of a vehicle to determine a rider state and optimizing at least one operating parameter of the vehicle to improve the rider state.

0243An aspect provided herein includes a rider satisfaction system <b>32123</b> for optimizing rider satisfaction <b>32121</b>, the rider satisfaction system comprising: an electronic commerce interface <b>32141</b> deployed for access by a rider in a vehicle <b>3210</b>; a rider interaction circuit that captures rider interactions with the deployed interface <b>32141</b>; a rider state determination circuit <b>32143</b> that processes the captured rider interactions <b>32144</b> to determine a rider state <b>32145</b>; and an artificial intelligence system <b>3236</b> trained to optimize, responsive to a rider state <b>3237</b>, at least one parameter <b>32124</b> affecting operation of the vehicle to improve the rider state <b>3237</b>. In embodiments, the vehicle <b>3210</b> comprises a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle is at least a semi-autonomous vehicle. In embodiments, the vehicle is automatically routed. In embodiments, the vehicle is a self-driving vehicle. In embodiments, the electronic commerce interface is self-adaptive and responsive to at least one of an identity of the rider, a route of the vehicle, a rider mood, rider behavior, vehicle configuration, and vehicle state.

0244In embodiments, the electronic commerce interface <b>32141</b> provides in-vehicle-relevant content <b>32146</b> that is based on at least one of an identity of the rider, a route of the vehicle, a rider mood, rider behavior, vehicle configuration, and vehicle state. In embodiments, the electronic commerce interface executes a user interaction workflow <b>32147</b> adapted for use by a rider <b>3244</b> in a vehicle <b>3210</b>. In embodiments, the electronic commerce interface provides one or more results of a search query <b>32148</b> that are adapted for presentation in a vehicle. In embodiments, the search query results adapted for presentation in a vehicle are presented in the electronic commerce interface along with advertising adapted for presentation in a vehicle. In embodiments, the rider interaction circuit <b>32142</b> captures rider interactions <b>32144</b> with the interface responsive to content <b>32146</b> presented in the interface.

0245<figref idref="DRAWINGS">FIG. <b>33</b></figref> illustrates a method <b>3300</b> for optimizing a parameter of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At <b>3302</b> the method includes capturing rider interactions with an in-vehicle electronic commerce system. At <b>3304</b> the method includes determining a rider state based on the captured rider interactions and a least one operating parameter of the vehicle. At <b>3306</b> the method includes processing the rider state with a rider satisfaction model that is adapted to suggest at least one operating parameter of a vehicle the influences the rider state. At <b>3308</b> the method includes optimizing the suggested at least one operating parameter for at least one of maintaining and improving a rider state.

0246Referring to <figref idref="DRAWINGS">FIG. <b>32</b></figref> and <figref idref="DRAWINGS">FIG. <b>33</b></figref>, an aspect provided herein includes an artificial intelligence system <b>3236</b> for improving rider satisfaction, comprising: a first neural network <b>3222</b> trained to classify rider states based on analysis of rider interactions <b>32144</b> with an in-vehicle electronic commerce system to detect a rider state <b>32149</b> through recognition of aspects of the rider interactions <b>32144</b> captured while the rider is occupying the vehicle that correlate to at least one state <b>3237</b> of the rider; and a second neural network <b>3220</b> that optimizes, for achieving a favorable state of the rider, an operational parameter of the vehicle in response to the detected state of the rider.

0247Referring to <figref idref="DRAWINGS">FIG. <b>34</b></figref>, in embodiments provided herein are transportation systems <b>3411</b> having an artificial intelligence system <b>3436</b> for processing data from at least one Internet of Things (IoT) device <b>34150</b> in the environment <b>34151</b> of a vehicle <b>3410</b> to determine a state <b>34152</b> of the vehicle and optimizing at least one operating parameter <b>34124</b> of the vehicle to improve a rider's state <b>3437</b> based on the determined state <b>34152</b> of the vehicle.

0248An aspect provided herein includes a system for transportation <b>3411</b>, comprising: an artificial intelligence system <b>3436</b> for processing data from at least one Internet of Things device <b>34150</b> in an environment <b>34151</b> of a vehicle <b>3410</b> to determine a determined state <b>34152</b> of the vehicle and optimizing at least one operating parameter <b>34124</b> of the vehicle to improve a state <b>3437</b> of the rider based on the determined state <b>34152</b> of the vehicle <b>3410</b>.

0249<figref idref="DRAWINGS">FIG. <b>35</b></figref> illustrates a method <b>3500</b> for improving a state of a rider through optimization of operation of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At <b>3502</b> the method includes capturing vehicle operation-related data with at least one Internet-of-things device. At <b>3504</b> the method includes analyzing the captured data with a first neural network that determines a state of the vehicle based at least in part on a portion of the captured vehicle operation-related data. At <b>3506</b> the method includes receiving data descriptive of a state of a rider occupying the operating vehicle. At <b>3508</b> the method includes using a neural network to determine at least one vehicle operating parameter that affects a state of a rider occupying the operating vehicle. At <b>3509</b> the method includes using an artificial intelligence-based system to optimize the at least one vehicle operating parameter so that a result of the optimizing comprises an improvement in the state of the rider.

0250Referring to <figref idref="DRAWINGS">FIG. <b>34</b></figref> and <figref idref="DRAWINGS">FIG. <b>35</b></figref>, in embodiments, the vehicle <b>3410</b> comprises a system for automating at least one control parameter <b>34153</b> of the vehicle <b>3410</b>. In embodiments, the vehicle <b>3410</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>3410</b> is automatically routed. In embodiments, the vehicle <b>3410</b> is a self-driving vehicle. In embodiments, the at least one Internet-of-things device <b>34150</b> is disposed in an operating environment <b>34154</b> of the vehicle. In embodiments, the at least one Internet-of-things device <b>34150</b> that captures the data about the vehicle <b>3410</b> is disposed external to the vehicle <b>3410</b>. In embodiments, the at least one Internet-of-things device is a dashboard camera. In embodiments, the at least one Internet-of-things device is a mirror camera. In embodiments, the at least one Internet-of-things device is a motion sensor. In embodiments, the at least one Internet-of-things device is a seat-based sensor system. In embodiments, the at least one Internet-of-things device is an IoT enabled lighting system. In embodiments, the lighting system is a vehicle interior lighting system. In embodiments, the lighting system is a headlight lighting system. In embodiments, the at least one Internet-of-things device is a traffic light camera or sensor. In embodiments, the at least one Internet-of-things device is a roadway camera. In embodiments, the roadway camera is disposed on at least one of a telephone phone and a light pole. In embodiments, the at least one Internet-of-things device is an in-road sensor. In embodiments, the at least one Internet-of-things device is an in-vehicle thermostat. In embodiments, the at least one Internet-of-things device is a toll booth. In embodiments, the at least one Internet-of-things device is a street sign. In embodiments, the at least one Internet-of-things device is a traffic control light. In embodiments, the at least one Internet-of-things device is a vehicle mounted sensor. In embodiments, the at least one Internet-of-things device is a refueling system. In embodiments, the at least one Internet-of-things device is a recharging system. In embodiments, the at least one Internet-of-things device is a wireless charging station.

0251An aspect provided herein includes a rider state modification system <b>34155</b> for improving a state <b>3437</b> of a rider <b>3444</b> in a vehicle <b>3410</b>, the system comprising: a first neural network <b>3422</b> that operates to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet-of-things device <b>34150</b> during operation of the vehicle <b>3410</b>; and a second neural network <b>3420</b> that operates to optimize at least one operating parameter <b>34124</b> of the vehicle based on the classified state <b>34152</b> of the vehicle, information about a state of a rider occupying the vehicle, and information that correlates vehicle operation with an effect on rider state.

0252In embodiments, the vehicle comprises a system for automating at least one control parameter <b>34153</b> of the vehicle <b>3410</b>. In embodiments, the vehicle <b>3410</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>3410</b> is automatically routed. In embodiments, the vehicle <b>3410</b> is a self-driving vehicle. In embodiments, the at least one Internet-of-things device <b>34150</b> is disposed in an operating environment of the vehicle <b>3410</b>. In embodiments, the at least one Internet-of-things device <b>34150</b> that captures the data about the vehicle <b>3410</b> is disposed external to the vehicle <b>3410</b>. In embodiments, the at least one Internet-of-things device is a dashboard camera. In embodiments, the at least one Internet-of-things device is a mirror camera. In embodiments, the at least one Internet-of-things device is a motion sensor. In embodiments, the at least one Internet-of-things device is a seat-based sensor system. In embodiments, the at least one Internet-of-things device is an IoT enabled lighting system.

0253In embodiments, the lighting system is a vehicle interior lighting system. In embodiments, the lighting system is a headlight lighting system. In embodiments, the at least one Internet-of-things device is a traffic light camera or sensor. In embodiments, the at least one Internet-of-things device is a roadway camera. In embodiments, the roadway camera is disposed on at least one of a telephone phone and a light pole. In embodiments, the at least one Internet-of-things device is an in-road sensor. In embodiments, the at least one Internet-of-things device is an in-vehicle thermostat. In embodiments, the at least one Internet-of-things device is a toll booth. In embodiments, the at least one Internet-of-things device is a street sign. In embodiments, the at least one Internet-of-things device is a traffic control light. In embodiments, the at least one Internet-of-things device is a vehicle mounted sensor. In embodiments, the at least one Internet-of-things device is a refueling system. In embodiments, the at least one Internet-of-things device is a recharging system. In embodiments, the at least one Internet-of-things device is a wireless charging station.

0254An aspect provided herein includes an artificial intelligence system <b>3436</b> comprising: a first neural network <b>3422</b> trained to determine an operating state <b>34152</b> of a vehicle <b>3410</b> from data about the vehicle captured in an operating environment <b>34154</b> of the vehicle, wherein the first neural network <b>3422</b> operates to identify an operating state <b>34152</b> of the vehicle by processing information about the vehicle <b>3410</b> that is captured by at least one Internet-of things device <b>34150</b> while the vehicle is operating; a data structure <b>34156</b> that facilitates determining operating parameters that influence an operating state of a vehicle; a second neural network <b>3420</b> that operates to optimize at least one of the determined operating parameters <b>34124</b> of the vehicle based on the identified operating state <b>34152</b> by processing information about a state of a rider <b>3444</b> occupying the vehicle <b>3410</b>, and information that correlates vehicle operation with an effect on rider state.

0255In embodiments, the improvement in the state of the rider is reflected in updated data that is descriptive of a state of the rider captured responsive to the vehicle operation based on the optimized at least one vehicle operating parameter. In embodiments, the improvement in the state of the rider is reflected in data captured by at least one Internet-of-things device <b>34150</b> disposed to capture information about the rider <b>3444</b> while occupying the vehicle <b>3410</b> responsive to the optimizing. In embodiments, the vehicle <b>3410</b> comprises a system for automating at least one control parameter <b>34153</b> of the vehicle. In embodiments, the vehicle <b>3410</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>3410</b> is automatically routed. In embodiments, the vehicle <b>3410</b> is a self-driving vehicle. In embodiments, the at least one Internet-of-things device <b>34150</b> is disposed in an operating environment <b>34154</b> of the vehicle. In embodiments, the at least one Internet-of-things device <b>34150</b> that captures the data about the vehicle is disposed external to the vehicle. In embodiments, the at least one Internet-of-things device <b>34150</b> is a dashboard camera. In embodiments, the at least one Internet-of-things device <b>34150</b> is a mirror camera. In embodiments, the at least one Internet-of-things device <b>34150</b> is a motion sensor. In embodiments, the at least one Internet-of-things device <b>34150</b> is a seat-based sensor system. In embodiments, the at least one Internet-of-things device <b>34150</b> is an IoT enabled lighting system.

0256In embodiments, the lighting system is a vehicle interior lighting system. In embodiments, the lighting system is a headlight lighting system. In embodiments, the at least one Internet-of-things device <b>34150</b> is a traffic light camera or sensor. In embodiments, the at least one Internet-of-things device <b>34150</b> is a roadway camera. In embodiments, the roadway camera is disposed on at least one of a telephone phone and a light pole. In embodiments, the at least one Internet-of-things device <b>34150</b> is an in-road sensor. In embodiments, the at least one Internet-of-things device <b>34150</b> is an in-vehicle thermostat. In embodiments, the at least one Internet-of-things device <b>34150</b> is a toll booth. In embodiments, the at least one Internet-of-things device <b>34150</b> is a street sign. In embodiments, the at least one Internet-of-things device <b>34150</b> is a traffic control light. In embodiments, the at least one Internet-of-things device <b>34150</b> is a vehicle mounted sensor. In embodiments, the at least one Internet-of-things device <b>34150</b> is a refueling system. In embodiments, the at least one Internet-of-things device <b>34150</b> is a recharging system. In embodiments, the at least one Internet-of-things device <b>34150</b> is a wireless charging station.

0257Referring to <figref idref="DRAWINGS">FIG. <b>36</b></figref>, in embodiments provided herein are transportation systems <b>3611</b> having an artificial intelligence system <b>3636</b> for processing a sensory input from a wearable device <b>36157</b> in a vehicle <b>3610</b> to determine an emotional state <b>36126</b> and optimizing at least one operating parameter <b>36124</b> of the vehicle <b>3610</b> to improve the rider's emotional state <b>3637</b>. A wearable device <b>36150</b>, such as any described throughout this disclosure, may be used to detect any of the emotional states described herein (favorable or unfavorable) and used both as an input to a real-time control system (such as a model-based, rule-based, or artificial intelligence system of any of the types described herein), such as to indicate an objective to improve an unfavorable state or maintain a favorable state, as well as a feedback mechanism to train an artificial intelligence system <b>3636</b> to configure sets of operating parameters <b>36124</b> to promote or maintain favorable states.

0258An aspect provided herein includes a system for transportation <b>3611</b>, comprising: an artificial intelligence system <b>3636</b> for processing a sensory input from a wearable device <b>36157</b> in a vehicle <b>3610</b> to determine an emotional state <b>36126</b> of a rider <b>3644</b> in the vehicle <b>3610</b> and optimizing an operating parameter <b>36124</b> of the vehicle to improve the emotional state <b>3637</b> of the rider <b>3644</b>. In embodiments, the vehicle is a self-driving vehicle. In embodiments, the artificial intelligence system <b>3636</b> is to detect the emotional state <b>36126</b> of the rider riding in the self-driving vehicle by recognition of patterns of emotional state indicative data from a set of wearable sensors <b>36157</b> worn by the rider <b>3644</b>. In embodiments, the patterns are indicative of at least one of a favorable emotional state of the rider and an unfavorable emotional state of the rider. In embodiments, the artificial intelligence system <b>3636</b> is to optimize, for achieving at least one of maintaining a detected favorable emotional state of the rider and achieving a favorable emotional state of a rider subsequent to a detection of an unfavorable emotional state, the operating parameter <b>36124</b> of the vehicle in response to the detected emotional state of the rider. In embodiments, the artificial intelligence system <b>3636</b> comprises an expert system that detects an emotional state of the rider by processing rider emotional state indicative data received from the set of wearable sensors <b>36157</b> worn by the rider. In embodiments, the expert system processes the rider emotional state indicative data using at least one of a training set of emotional state indicators of a set of riders and trainer-generated rider emotional state indicators. In embodiments, the artificial intelligence system comprises a recurrent neural network <b>3622</b> that detects the emotional state of the rider.

0259In embodiments, the recurrent neural network comprises a plurality of connected nodes that form a directed cycle, the recurrent neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the artificial intelligence system <b>3636</b> comprises a radial basis function neural network <b>3620</b> that optimizes the operational parameter <b>36124</b>. In embodiments, the optimizing an operational parameter <b>36124</b> is based on a correlation between a vehicle operating state <b>3645</b> and a rider emotional state <b>3637</b>. In embodiments, the correlation is determined using at least one of a training set of emotional state indicators of a set of riders and human trainer-generated rider emotional state indicators. In embodiments, the operational parameter of the vehicle that is optimized is determined and adjusted to induce a favorable rider emotional state.

0260In embodiments, the artificial intelligence system <b>3636</b> further learns to classify the patterns of the emotional state indicative data and associate the patterns to emotional states and changes thereto from a training data set <b>36131</b> sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. In embodiments, the artificial intelligence system <b>3636</b> detects a pattern of the rider emotional state indicative data that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state, the optimizing of the operational parameter of the vehicle being response to the indicated change in emotional state. In embodiments, the patterns of rider emotional state indicative data indicates at least one of an emotional state of the rider is changing, an emotional state of the rider is stable, a rate of change of an emotional state of the rider, a direction of change of an emotional state of the rider, and a polarity of a change of an emotional state of the rider; an emotional state of a rider is changing to an unfavorable state; and an emotional state of a rider is changing to a favorable state.

0261In embodiments, the operational parameter <b>36124</b> that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the artificial intelligence system <b>3636</b> interacts with a vehicle control system to optimize the operational parameter. In embodiments, the artificial intelligence system <b>3636</b> further comprises a neural net <b>3622</b> that includes one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the set of wearable sensors <b>36157</b> comprises at least two of a watch, a ring, a wrist band, an arm band, an ankle band, a torso band, a skin patch, a head-worn device, eye glasses, foot wear, a glove, an in-ear device, clothing, headphones, a belt, a finger ring, a thumb ring, a toe ring, and a necklace. In embodiments, the artificial intelligence system <b>3636</b> uses deep learning for determining patterns of wearable sensor-generated emotional state indicative data that indicate an emotional state of the rider as at least one of a favorable emotional state and an unfavorable emotional state. In embodiments, the artificial intelligence system <b>3636</b> is responsive to a rider indicated emotional state by at least optimizing the operation parameter to at least one of achieve and maintain the rider indicated emotional state.

0262In embodiments, the artificial intelligence system <b>3636</b> adapts a characterization of a favorable emotional state of the rider based on context gathered from a plurality of sources including data indicating a purpose of the rider riding in the self-driving vehicle, a time of day, traffic conditions, weather conditions and optimizes the operating parameter <b>36124</b> to at least one of achieve and maintain the adapted favorable emotional state. In embodiments, the artificial intelligence system <b>3636</b> optimizes the operational parameter in real time responsive to the detecting of an emotional state of the rider. In embodiments, the vehicle is a self-driving vehicle. In embodiments, the artificial intelligence system comprises: a first neural network <b>3622</b> to detect the emotional state of the rider through expert system-based processing of rider emotional state indicative wearable sensor data of a plurality of wearable physiological condition sensors worn by the rider in the vehicle, the emotional state indicative wearable sensor data indicative of at least one of a favorable emotional state of the rider and an unfavorable emotional state of the rider; and a second neural network <b>3620</b> to optimize, for at least one of achieving and maintaining a favorable emotional state of the rider, the operating parameter <b>36124</b> of the vehicle in response to the detected emotional state of the rider. In embodiments, the first neural network <b>3622</b> is a recurrent neural network and the second neural network <b>3620</b> is a radial basis function neural network.

0263In embodiments, the second neural network <b>3620</b> optimizes the operational parameter <b>36124</b> based on a correlation between a vehicle operating state <b>3645</b> and a rider emotional state <b>3637</b>. In embodiments, the operational parameter of the vehicle that is optimized is determined and adjusted to induce a favorable rider emotional state. In embodiments, the first neural network <b>3622</b> further learns to classify patterns of the rider emotional state indicative wearable sensor data and associate the patterns to emotional states and changes thereto from a training data set sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. In embodiments, the second neural network <b>3620</b> optimizes the operational parameter in real time responsive to the detecting of an emotional state of the rider by the first neural network <b>3622</b>. In embodiments, the first neural network <b>3622</b> detects a pattern of the rider emotional state indicative wearable sensor data that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the second neural network <b>3620</b> optimizes the operational parameter of the vehicle in response to the indicated change in emotional state.

0264In embodiments, the first neural network <b>3622</b> comprises a plurality of connected nodes that form a directed cycle, the first neural network <b>3622</b> further facilitating bi-directional flow of data among the connected nodes. In embodiments, the first neural network <b>3622</b> includes one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the rider emotional state indicative wearable sensor data indicates at least one of an emotional state of the rider is changing, an emotional state of the rider is stable, a rate of change of an emotional state of the rider, a direction of change of an emotional state of the rider, and a polarity of a change of an emotional state of the rider; an emotional state of a rider is changing to an unfavorable state; and an emotional state of a rider is changing to a favorable state. In embodiments, the operational parameter that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the second neural network <b>3620</b> interacts with a vehicle control system to adjust the operational parameter. In embodiments, the first neural network <b>3622</b> includes one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated.

0265In embodiments, the vehicle is a self-driving vehicle. In embodiments, the artificial intelligence system <b>3636</b> is to detect a change in the emotional state of the rider riding in the self-driving vehicle at least in part by recognition of patterns of emotional state indicative data from a set of wearable sensors worn by the rider. In embodiments, the patterns are indicative of at least one of a diminishing of a favorable emotional state of the rider and an onset of an unfavorable emotional state of the rider. In embodiments, the artificial intelligence system <b>3636</b> is to determine at least one operating parameter <b>36124</b> of the self-driving vehicle that is indicative of the change in emotional state based on a correlation of the patterns of emotional state indicative data with a set of operating parameters of the vehicle. In embodiments, the artificial intelligence system <b>3636</b> is to determine an adjustment of the at least one operating parameter <b>36124</b> for achieving at least one of restoring the favorable emotional state of the rider and achieving a reduction in the onset of the unfavorable emotional state of a rider.

0266In embodiments, the correlation of patterns of rider emotional indicative state wearable sensor data is determined using at least one of a training set of emotional state wearable sensor indicators of a set of riders and human trainer-generated rider emotional state wearable sensor indicators. In embodiments, the artificial intelligence system <b>3636</b> further learns to classify the patterns of the emotional state indicative wearable sensor data and associate the patterns to changes in rider emotional states from a training data set sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. In embodiments, the patterns of rider emotional state indicative wearable sensor data indicates at least one of an emotional state of the rider is changing, an emotional state of the rider is stable, a rate of change of an emotional state of the rider, a direction of change of an emotional state of the rider, and a polarity of a change of an emotional state of the rider; an emotional state of a rider is changing to an unfavorable state; and an emotional state of a rider is changing to a favorable state.

0267In embodiments, the operational parameter determined from a result of processing the rider emotional state indicative wearable sensor data affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the artificial intelligence system <b>3636</b> further interacts with a vehicle control system for adjusting the operational parameter. In embodiments, the artificial intelligence system <b>3636</b> further comprises a neural net that includes one or more perceptrons that mimic human senses that facilitate determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated.

0268In embodiments, the set of wearable sensors comprises at least two of a watch, a ring, a wrist band, an arm band, an ankle band, a torso band, a skin patch, a head-worn device, eye glasses, foot wear, a glove, an in-ear device, clothing, headphones, a belt, a finger ring, a thumb ring, a toe ring, and a necklace. In embodiments, the artificial intelligence system <b>3636</b> uses deep learning for determining patterns of wearable sensor-generated emotional state indicative data that indicate the change in the emotional state of the rider. In embodiments, the artificial intelligence system <b>3636</b> further determines the change in emotional state of the rider based on context gathered from a plurality of sources including data indicating a purpose of the rider riding in the self-driving vehicle, a time of day, traffic conditions, weather conditions and optimizes the operating parameter <b>36124</b> to at least one of achieve and maintain the adapted favorable emotional state. In embodiments, the artificial intelligence system <b>3636</b> adjusts the operational parameter in real time responsive to the detecting of a change in rider emotional state.

0269In embodiments, the vehicle is a self-driving vehicle. In embodiments, the artificial intelligence system <b>3636</b> includes: a recurrent neural network to indicate a change in the emotional state of a rider in the self-driving vehicle by a recognition of patterns of emotional state indicative wearable sensor data from a set of wearable sensors worn by the rider. In embodiments, the patterns are indicative of at least one of a first degree of an favorable emotional state of the rider and a second degree of an unfavorable emotional state of the rider; and a radial basis function neural network to optimize, for achieving a target emotional state of the rider, the operating parameter <b>36124</b> of the vehicle in response to the indication of the change in the emotional state of the rider.

0270In embodiments, the radial basis function neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a rider emotional state. In embodiments, the target emotional state is a favorable rider emotional state and the operational parameter of the vehicle that is optimized is determined and adjusted to induce the favorable rider emotional state. In embodiments, the recurrent neural network further learns to classify the patterns of emotional state indicative wearable sensor data and associate them to emotional states and changes thereto from a training data set sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. In embodiments, the radial basis function neural network optimizes the operational parameter in real time responsive to the detecting of a change in an emotional state of the rider by the recurrent neural network. In embodiments, the recurrent neural network detects a pattern of the emotional state indicative wearable sensor data that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the radial basis function neural network optimizes the operational parameter of the vehicle in response to the indicated change in emotional state. In embodiments, the recurrent neural network comprises a plurality of connected nodes that form a directed cycle, the recurrent neural network further facilitating bi-directional flow of data among the connected nodes.

0271In embodiments, the patterns of emotional state indicative wearable sensor data indicate at least one of an emotional state of the rider is changing, an emotional state of the rider is stable, a rate of change of an emotional state of the rider, a direction of change of an emotional state of the rider, and a polarity of a change of an emotional state of the rider; an emotional state of a rider is changing to an unfavorable state; and an emotional state of a rider is changing to a favorable state. In embodiments, the operational parameter that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the radial basis function neural network interacts with a vehicle control system to adjust the operational parameter. In embodiments, the recurrent neural net includes one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated.

0272In embodiments, the artificial intelligence system <b>3636</b> is to maintain a favorable emotional state of the rider through use of a modular neural network, the modular neural network comprising: a rider emotional state determining neural network to process emotional state indicative wearable sensor data of a rider in the vehicle to detect patterns. In embodiments, the patterns found in the emotional state indicative wearable sensor data are indicative of at least one of a favorable emotional state of the rider and an unfavorable emotional state of the rider; an intermediary circuit to convert output data from the rider emotional state determining neural network into vehicle operational state data; and a vehicle operational state optimizing neural network to adjust the operating parameter <b>36124</b> of the vehicle in response to the vehicle operational state data.

0273In embodiments, the vehicle operational state optimizing neural network adjusts an operational parameter of the vehicle for achieving a favorable emotional state of the rider. In embodiments, the vehicle operational state optimizing neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a rider emotional state. In embodiments, the operational parameter of the vehicle that is optimized is determined and adjusted to induce a favorable rider emotional state. In embodiments, the rider emotional state determining neural network further learns to classify the patterns of emotional state indicative wearable sensor data and associate them to emotional states and changes thereto from a training data set sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system.

0274In embodiments, the vehicle operational state optimizing neural network optimizes the operational parameter in real time responsive to the detecting of a change in an emotional state of the rider by the rider emotional state determining neural network. In embodiments, the rider emotional state determining neural network detects a pattern of emotional state indicative wearable sensor data that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the vehicle operational state optimizing neural network optimizes the operational parameter of the vehicle in response to the indicated change in emotional state. In embodiments, the artificial intelligence system <b>3636</b> comprises a plurality of connected nodes that forms a directed cycle, the artificial intelligence system <b>3636</b> further facilitating bi-directional flow of data among the connected nodes. In embodiments, the pattern of emotional state indicative wearable sensor data indicate at least one of an emotional state of the rider is changing, an emotional state of the rider is stable, a rate of change of an emotional state of the rider, a direction of change of an emotional state of the rider, and a polarity of a change of an emotional state of the rider; an emotional state of a rider is changing to an unfavorable state; and an emotional state of a rider is changing to a favorable state.

0275In embodiments, the operational parameter that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the vehicle operational state optimizing neural network interacts with a vehicle control system to adjust the operational parameter. In embodiments, the artificial intelligence system <b>3636</b> further comprises a neural net that includes one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the rider emotional state determining neural network comprises one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated.

0276In embodiments, the artificial intelligence system <b>3636</b> is to indicate a change in the emotional state of a rider in the vehicle through recognition of patterns of emotional state indicative wearable sensor data of the rider in the vehicle; the transportation system further comprising: a vehicle control system to control an operation of the vehicle by adjusting a plurality of vehicle operating parameters; and a feedback loop through which the indication of the change in the emotional state of the rider is communicated between the vehicle control system and the artificial intelligence system <b>3636</b>. In embodiments, the vehicle control system adjusts at least one of the plurality of vehicle operating parameters responsive to the indication of the change. In embodiments, the vehicle controls system adjusts the at least one of the plurality of vehicle operational parameters based on a correlation between vehicle operational state and rider emotional state.

0277In embodiments, the vehicle control system adjusts the at least one of the plurality of vehicle operational parameters that are indicative of a favorable rider emotional state. In embodiments, the vehicle control system selects an adjustment of the at least one of the plurality of vehicle operational parameters that is indicative of producing a favorable rider emotional state. In embodiments, the artificial intelligence system <b>3636</b> further learns to classify the patterns of emotional state indicative wearable sensor data and associate them to emotional states and changes thereto from a training data set sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. In embodiments, the vehicle control system adjusts the at least one of the plurality of vehicle operation parameters in real time.

0278In embodiments, the artificial intelligence system <b>3636</b> further detects a pattern of the emotional state indicative wearable sensor data that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the vehicle operation control system adjusts an operational parameter of the vehicle in response to the indicated change in emotional state. In embodiments, the artificial intelligence system <b>3636</b> comprises a plurality of connected nodes that form a directed cycle, the artificial intelligence system <b>3636</b> further facilitating bi-directional flow of data among the connected nodes. In embodiments, the at least one of the plurality of vehicle operation parameters that is responsively adjusted affects operation of a powertrain of the vehicle and a suspension system of the vehicle.

0279In embodiments, the radial basis function neural network interacts with the recurrent neural network via an intermediary component of the artificial intelligence system <b>3636</b> that produces vehicle control data indicative of an emotional state response of the rider to a current operational state of the vehicle. In embodiments, the artificial intelligence system <b>3636</b> further comprises a modular neural network comprising a rider emotional state recurrent neural network for indicating the change in the emotional state of a rider, a vehicle operational state radial based function neural network, and an intermediary system. In embodiments, the intermediary system processes rider emotional state characterization data from the recurrent neural network into vehicle control data that the radial based function neural network uses to interact with the vehicle control system for adjusting the at least one operational parameter.

0280In embodiments, the artificial intelligence system <b>3636</b> comprises a neural net that includes one or more perceptrons that mimic human senses that facilitate determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the recognition of patterns of emotional state indicative wearable sensor data comprises processing the emotional state indicative wearable sensor data captured during at least two of before the adjusting at least one of the plurality of vehicle operational parameters, during the adjusting at least one of the plurality of vehicle operational parameters, and after adjusting at least one of the plurality of vehicle operational parameters.

0281In embodiments, the artificial intelligence system <b>3636</b> indicates a change in the emotional state of the rider responsive to a change in an operating parameter <b>36124</b> of the vehicle by determining a difference between a first set of emotional state indicative wearable sensor data of a rider captured prior to the adjusting at least one of the plurality of operating parameters and a second set of emotional state indicative wearable sensor data of the rider captured during or after the adjusting at least one of the plurality of operating parameters.

0282Referring to <figref idref="DRAWINGS">FIG. <b>37</b></figref>, in embodiments provided herein are transportation systems <b>3711</b> having a cognitive system <b>37158</b> for managing an advertising market for in-seat advertising for riders <b>3744</b> of self-driving vehicles. In embodiments, the cognitive system <b>37158</b> takes inputs relating to at least one parameter <b>37124</b> of the vehicle and/or the rider <b>3744</b> to determine at least one of a price, a type and a location of an advertisement to be delivered within an interface <b>37133</b> to a rider <b>3744</b> in a seat <b>3728</b> of the vehicle. As described above in connection with search, in-vehicle riders, particularly in self-driving vehicles, may be situationally disposed quite differently toward advertising when riding in a vehicle than at other times. Bored riders may be more willing to watch advertising content, click on offers or promotions, engage in surveys, or the like. In embodiments, an advertising marketplace platform may segment and separately handle advertising placements (including handling bids and asks for advertising placement and the like) for in-vehicle ads. Such an advertising marketplace platform may use information that is unique to a vehicle, such as vehicle type, display type, audio system capabilities, screen size, rider demographic information, route information, location information, and the like when characterizing advertising placement opportunities, such that bids for in-vehicle advertising placement reflect such vehicle, rider and other transportation-related parameters. For example, an advertiser may bid for placement of advertising on in-vehicle display systems of self-driving vehicles that are worth more than $50,000 and that are routed north on highway <b>101</b> during the morning commute. The advertising marketplace platform may be used to configure many such vehicle-related placement opportunities, to handle bidding for such opportunities, to place advertisements (such as by load-balanced servers that cache the ads) and to resolve outcomes. Yield metrics may be tracked and used to optimize configuration of the marketplace.

0283An aspect provided herein includes a system for transportation, comprising: a cognitive system <b>37158</b> for managing an advertising market for in-seat advertising for riders of self-driving vehicles, wherein the cognitive system <b>37158</b> takes inputs corresponding to at least one parameter <b>37159</b> of the vehicle or the rider <b>3744</b> to determine a characteristic <b>37160</b> of an advertisement to be delivered within an interface <b>37133</b> to a rider <b>3744</b> in a seat <b>3728</b> of the vehicle, wherein the characteristic <b>37160</b> of the advertisement is selected from the group consisting of a price, a category, a location and combinations thereof.

0284<figref idref="DRAWINGS">FIG. <b>38</b></figref> illustrates a method <b>3800</b> of vehicle in-seat advertising in accordance with embodiments of the systems and methods disclosed herein. At <b>3802</b> the method includes taking inputs relating to at least one parameter of a vehicle. At <b>3804</b> the method includes taking inputs relating to at least one parameter of a rider occupying the vehicle. At <b>3806</b> the method includes determining at least one of a price, classification, content, and location of an advertisement to be delivered within an interface of the vehicle to a rider in a seat in the vehicle based on the vehicle-related inputs and the rider-related inputs.

0285Referring to <figref idref="DRAWINGS">FIG. <b>37</b></figref> and <figref idref="DRAWINGS">FIG. <b>38</b></figref>, in embodiments, the vehicle <b>3710</b> is automatically routed. In embodiments, the vehicle <b>3710</b> is a self-driving vehicle. In embodiments, the cognitive system <b>37158</b> further determines at least one of a price, classification, content and location of an advertisement placement. In embodiments, an advertisement is delivered from an advertiser who places a winning bid. In embodiments, delivering an advertisement is based on a winning bid. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include vehicle classification. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include display classification. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include audio system capability. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include screen size.

0286In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include route information. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include location information. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider demographic information. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider emotional state. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider response to prior in-seat advertising. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider social media activity.

0287<figref idref="DRAWINGS">FIG. <b>39</b></figref> illustrates a method <b>3900</b> of in-vehicle advertising interaction tracking in accordance with embodiments of the systems and methods disclosed herein. At <b>3902</b> the method includes taking inputs relating to at least one parameter of a vehicle and inputs relating to at least one parameter of a rider occupying the vehicle. At <b>3904</b> the method includes aggregating the inputs across a plurality of vehicles. At <b>3906</b> the method includes using a cognitive system to determine opportunities for in-vehicle advertisement placement based on the aggregated inputs. At <b>3907</b> the method includes offering the placement opportunities in an advertising network that facilitates bidding for the placement opportunities. At <b>3908</b> the method includes based on a result of the bidding, delivering an advertisement for placement within a user interface of the vehicle. At <b>3909</b> the method includes monitoring vehicle rider interaction with the advertisement presented in the user interface of the vehicle.

0288Referring to <figref idref="DRAWINGS">FIGS. <b>37</b> and <b>39</b></figref>, in embodiments, the vehicle <b>3710</b> comprises a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle <b>3710</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>3710</b> is automatically routed. In embodiments, the vehicle <b>3710</b> is a self-driving vehicle. In embodiments, an advertisement is delivered from an advertiser who places a winning bid. In embodiments, delivering an advertisement is based on a winning bid. In embodiments, the monitored vehicle rider interaction information includes information for resolving click-based payments. In embodiments, the monitored vehicle rider interaction information includes an analytic result of the monitoring. In embodiments, the analytic result is a measure of interest in the advertisement. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include vehicle classification.

0289In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include display classification. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include audio system capability. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include screen size. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include route information. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include location information. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider demographic information. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider emotional state. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider response to prior in-seat advertising. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider social media activity.

0290<figref idref="DRAWINGS">FIG. <b>40</b></figref> illustrates a method <b>4000</b> of in-vehicle advertising in accordance with embodiments of the systems and methods disclosed herein. At <b>4002</b> the method includes taking inputs relating to at least one parameter of a vehicle and inputs relating to at least one parameter of a rider occupying the vehicle. At <b>4004</b> the method includes aggregating the inputs across a plurality of vehicles. At <b>4006</b> the method includes using a cognitive system to determine opportunities for in-vehicle advertisement placement based on the aggregated inputs. At <b>4008</b> the method includes offering the placement opportunities in an advertising network that facilitates bidding for the placement opportunities. At <b>4009</b> the method includes based on a result of the bidding, delivering an advertisement for placement within an interface of the vehicle.

0291Referring to <figref idref="DRAWINGS">FIG. <b>37</b></figref> and <figref idref="DRAWINGS">FIG. <b>40</b></figref>, in embodiments, the vehicle <b>3710</b> comprises a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle <b>3710</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>3710</b> is automatically routed. In embodiments, the vehicle <b>3710</b> is a self-driving vehicle. In embodiments, the cognitive system <b>37158</b> further determines at least one of a price, classification, content and location of an advertisement placement. In embodiments, an advertisement is delivered from an advertiser who places a winning bid. In embodiments, delivering an advertisement is based on a winning bid. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include vehicle classification.

0292In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include display classification. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include audio system capability. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include screen size. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include route information. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include location information. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider demographic information. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider emotional state. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider response to prior in-seat advertising. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider social media activity.

0293An aspect provided herein includes an advertising system of vehicle in-seat advertising, the advertising system comprising: a cognitive system <b>37158</b> that takes inputs <b>37162</b> relating to at least one parameter <b>37124</b> of a vehicle <b>3710</b> and takes inputs relating to at least one parameter <b>37161</b> of a rider occupying the vehicle, and determines at least one of a price, classification, content and location of an advertisement to be delivered within an interface <b>37133</b> of the vehicle <b>3710</b> to a rider <b>3744</b> in a seat <b>3728</b> in the vehicle <b>3710</b> based on the vehicle-related inputs <b>37162</b> and the rider-related inputs <b>37163</b>.

0294In embodiments, the vehicle <b>4110</b> comprises a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle <b>4110</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>4110</b> is automatically routed. In embodiments, the vehicle <b>4110</b> is a self-driving vehicle. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include vehicle classification. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include display classification. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include audio system capability. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include screen size. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include route information. In embodiments, the inputs <b>37162</b> relating to the at least one parameter of a vehicle include location information. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider demographic information. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider emotional state. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider response to prior in-seat advertising. In embodiments, the inputs <b>37163</b> relating to the at least one parameter of a rider include rider social media activity.

0295In embodiments, the advertising system is further to determine a vehicle operating state from the inputs <b>37162</b> related to at least one parameter of the vehicle. In embodiments, the advertisement to be delivered is determined based at least in part on the determined vehicle operating state. In embodiments, the advertising system is further to determine a rider state <b>37149</b> from the inputs <b>37163</b> related to at least one parameter of the rider. In embodiments, the advertisement to be delivered is determined based at least in part on the determined rider state <b>37149</b>.

0296Referring to <figref idref="DRAWINGS">FIG. <b>41</b></figref>, in embodiments provided herein are transportation systems <b>4111</b> having a hybrid cognitive system <b>41164</b> for managing an advertising market for in-seat advertising to riders of vehicles <b>4110</b>. In embodiments, at least one part of the hybrid cognitive system <b>41164</b> processes inputs <b>41162</b> relating to at least one parameter <b>41124</b> of the vehicle to determine a vehicle operating state and at least one other part of the cognitive system processes inputs relating to a rider to determine a rider state. In embodiments, the cognitive system determines at least one of a price, a type and a location of an advertisement to be delivered within an interface to a rider in a seat of the vehicle.

0297An aspect provided herein includes a system for transportation <b>4111</b>, comprising: a hybrid cognitive system <b>41164</b> for managing an advertising market for in-seat advertising to riders <b>4144</b> of vehicles <b>4110</b>. In embodiments, at least one part <b>41165</b> of the hybrid cognitive system processes inputs <b>41162</b> corresponding to at least one parameter of the vehicle to determine a vehicle operating state <b>41168</b> and at least one other part <b>41166</b> of the cognitive system <b>41164</b> processes inputs <b>41163</b> relating to a rider to determine a rider state <b>41149</b>. In embodiments, the cognitive system <b>41164</b> determines a characteristic <b>41160</b> of an advertisement to be delivered within an interface <b>41133</b> to the rider <b>4144</b> in a seat <b>4128</b> of the vehicle <b>4110</b>. In embodiments, the characteristic <b>41160</b> of the advertisement is selected from the group consisting of a price, a category, a location and combinations thereof.

0298An aspect provided herein includes an artificial intelligence system <b>4136</b> for vehicle in-seat advertising, comprising: a first portion <b>41165</b> of the artificial intelligence system <b>4136</b> that determines a vehicle operating state <b>41168</b> of the vehicle by processing inputs <b>41162</b> relating to at least one parameter of the vehicle; a second portion <b>41166</b> of the artificial intelligence system <b>4136</b> that determines a state <b>41149</b> of the rider of the vehicle by processing inputs <b>41163</b> relating to at least one parameter of the rider; and a third portion <b>41167</b> of the artificial intelligence system <b>4136</b> that determines at least one of a price, classification, content and location of an advertisement to be delivered within an interface <b>41133</b> of the vehicle to a rider <b>4144</b> in a seat in the vehicle <b>4110</b> based on the vehicle (operating) state <b>41168</b> and the rider state <b>41149</b>.

0299In embodiments, the vehicle <b>4110</b> comprises a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle is at least a semi-autonomous vehicle. In embodiments, the vehicle is automatically routed. In embodiments, the vehicle is a self-driving vehicle. In embodiments, the cognitive system <b>41164</b> further determines at least one of a price, classification, content and location of an advertisement placement. In embodiments, an advertisement is delivered from an advertiser who places a winning bid. In embodiments, delivering an advertisement is based on a winning bid. In embodiments, the inputs relating to the at least one parameter of a vehicle include vehicle classification.

0300In embodiments, the inputs relating to the at least one parameter of a vehicle include display classification. In embodiments, the inputs relating to the at least one parameter of a vehicle include audio system capability. In embodiments, the inputs relating to the at least one parameter of a vehicle include screen size. In embodiments, the inputs relating to the at least one parameter of a vehicle include route information. In embodiments, the inputs relating to the at least one parameter of a vehicle include location information. In embodiments, the inputs relating to the at least one parameter of a rider include rider demographic information. In embodiments, the inputs relating to the at least one parameter of a rider include rider emotional state. In embodiments, the inputs relating to the at least one parameter of a rider include rider response to prior in-seat advertising. In embodiments, the inputs relating to the at least one parameter of a rider include rider social media activity.

0301<figref idref="DRAWINGS">FIG. <b>42</b></figref> illustrates a method <b>4200</b> of in-vehicle advertising interaction tracking in accordance with embodiments of the systems and methods disclosed herein. At <b>4202</b> the method includes taking inputs relating to at least one parameter of a vehicle and inputs relating to at least one parameter of a rider occupying the vehicle. At <b>4204</b> the method includes aggregating the inputs across a plurality of vehicles. At <b>4206</b> the method includes using a hybrid cognitive system to determine opportunities for in-vehicle advertisement placement based on the aggregated inputs. At <b>4207</b> the method includes offering the placement opportunities in an advertising network that facilitates bidding for the placement opportunities. At <b>4208</b> the method includes based on a result of the bidding, delivering an advertisement for placement within a user interface of the vehicle. At <b>4209</b> the method includes monitoring vehicle rider interaction with the advertisement presented in the user interface of the vehicle.

0302Referring to <figref idref="DRAWINGS">FIG. <b>41</b></figref> and <figref idref="DRAWINGS">FIG. <b>42</b></figref>, in embodiments, the vehicle <b>4110</b> comprises a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle <b>4110</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>4110</b> is automatically routed. In embodiments, the vehicle <b>4110</b> is a self-driving vehicle. In embodiments, a first portion <b>41165</b> of the hybrid cognitive system <b>41164</b> determines an operating state of the vehicle by processing inputs relating to at least one parameter of the vehicle. In embodiments, a second portion <b>41166</b> of the hybrid cognitive system <b>41164</b> determines a state <b>41149</b> of the rider of the vehicle by processing inputs relating to at least one parameter of the rider. In embodiments, a third portion <b>41167</b> of the hybrid cognitive system <b>41164</b> determines at least one of a price, classification, content and location of an advertisement to be delivered within an interface of the vehicle to a rider in a seat in the vehicle based on the vehicle state and the rider state. In embodiments, an advertisement is delivered from an advertiser who places a winning bid. In embodiments, delivering an advertisement is based on a winning bid. In embodiments, the monitored vehicle rider interaction information includes information for resolving click-based payments. In embodiments, the monitored vehicle rider interaction information includes an analytic result of the monitoring. In embodiments, the analytic result is a measure of interest in the advertisement. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include vehicle classification. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include display classification. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include audio system capability. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include screen size. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include route information. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include location information. In embodiments, the inputs <b>41163</b> relating to the at least one parameter of a rider include rider demographic information. In embodiments, the inputs <b>41163</b> relating to the at least one parameter of a rider include rider emotional state. In embodiments, the inputs <b>41163</b> relating to the at least one parameter of a rider include rider response to prior in-seat advertising. In embodiments, the inputs <b>41163</b> relating to the at least one parameter of a rider include rider social media activity.

0303<figref idref="DRAWINGS">FIG. <b>43</b></figref> illustrates a method <b>4300</b> of in-vehicle advertising in accordance with embodiments of the systems and methods disclosed herein. At <b>4302</b> the method includes taking inputs relating to at least one parameter of a vehicle and inputs relating to at least one parameter of a rider occupying the vehicle. At <b>4304</b> the method includes aggregating the inputs across a plurality of vehicles. At <b>4306</b> the method includes using a hybrid cognitive system to determine opportunities for in-vehicle advertisement placement based on the aggregated inputs. At <b>4308</b> the method includes offering the placement opportunities in an advertising network that facilitates bidding for the placement opportunities. At <b>4309</b> the method includes based on a result of the bidding, delivering an advertisement for placement within an interface of the vehicle.

0304Referring to <figref idref="DRAWINGS">FIG. <b>41</b></figref> and <figref idref="DRAWINGS">FIG. <b>43</b></figref>, in embodiments, the vehicle <b>4110</b> comprises a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle <b>4110</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>4110</b> is automatically routed. In embodiments, the vehicle <b>4110</b> is a self-driving vehicle. In embodiments, a first portion <b>41165</b> of the hybrid cognitive system <b>41164</b> determines an operating state <b>41168</b> of the vehicle by processing inputs <b>41162</b> relating to at least one parameter of the vehicle. In embodiments, a second portion <b>41166</b> of the hybrid cognitive system <b>41164</b> determines a state <b>41149</b> of the rider of the vehicle by processing inputs <b>41163</b> relating to at least one parameter of the rider. In embodiments, a third portion <b>41167</b> of the hybrid cognitive system <b>41164</b> determines at least one of a price, classification, content and location of an advertisement to be delivered within an interface <b>41133</b> of the vehicle <b>4110</b> to a rider <b>4144</b> in a seat <b>4128</b> in the vehicle <b>4110</b> based on the vehicle (operating) state <b>41168</b> and the rider state <b>41149</b>. In embodiments, an advertisement is delivered from an advertiser who places a winning bid. In embodiments, delivering an advertisement is based on a winning bid. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include vehicle classification. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include display classification. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include audio system capability. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include screen size. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include route information. In embodiments, the inputs <b>41162</b> relating to the at least one parameter of a vehicle include location information. In embodiments, the inputs <b>41163</b> relating to the at least one parameter of a rider include rider demographic information. In embodiments, the inputs <b>41163</b> relating to the at least one parameter of a rider include rider emotional state. In embodiments, the inputs <b>41163</b> relating to the at least one parameter of a rider include rider response to prior in-seat advertising. In embodiments, the inputs <b>41163</b> relating to the at least one parameter of a rider include rider social media activity.

0305Referring to <figref idref="DRAWINGS">FIG. <b>44</b></figref>, in embodiments provided herein are transportation systems <b>4411</b> having a motorcycle helmet <b>44170</b> that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer <b>44172</b> in an environment <b>44171</b>.

0306An aspect provided herein includes a system for transportation <b>4411</b>, comprising: a motorcycle helmet <b>44170</b> to provide an augmented reality experience based on registration of a location and orientation of a wearer <b>44172</b> of the helmet <b>44170</b> in an environment <b>44171</b>.

0307An aspect provided herein includes a motorcycle helmet <b>44170</b> comprising: a data processor <b>4488</b> configured to facilitate communication between a rider <b>44172</b> wearing the helmet <b>44170</b> and a motorcycle <b>44169</b>, the motorcycle <b>44169</b> and the helmet <b>44170</b> communicating location and orientation <b>44173</b> of the motorcycle <b>44169</b>; and an augmented reality system <b>44174</b> with a display <b>44175</b> disposed to facilitate presenting an augmentation of content in an environment <b>44171</b> of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation <b>44128</b> of the motorcycle <b>44169</b>. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider <b>44172</b> and the motorcycle <b>44180</b>.

0308In embodiments, the motorcycle <b>44169</b> comprises a system for automating at least one control parameter of the motorcycle. In embodiments, the motorcycle <b>44169</b> is at least a semi-autonomous motorcycle. In embodiments, the motorcycle <b>44169</b> is automatically routed. In embodiments, the motorcycle <b>44169</b> is a self-driving motorcycle. In embodiments, the content in the environment is content that is visible in a portion of a field of view of the rider wearing the helmet. In embodiments, the machine learning on the input of the rider determines an emotional state of the rider and a value for the at least one parameter is adapted responsive to the rider emotional state. In embodiments, the machine learning on the input of the motorcycle determines an operational state of the motorcycle and a value for the at least one parameter is adapted responsive to the motorcycle operational state. In embodiments, the helmet <b>44170</b> further comprises a motorcycle configuration expert system <b>44139</b> for recommending an adjustment of a value of the at least one parameter <b>44156</b> to the augmented reality system responsive to the at least one input.

0309An aspect provided herein includes a motorcycle helmet augmented reality system comprising: a display <b>44175</b> disposed to facilitate presenting an augmentation of content in an environment of a rider wearing the helmet; a circuit <b>4488</b> for registering at least one of location and orientation of a motorcycle that the rider is riding; a machine learning circuit <b>44179</b> that determines at least one augmentation parameter <b>44156</b> by processing at least one input relating to at least one of the rider <b>44163</b> and the motorcycle <b>44180</b>; and a reality augmentation circuit <b>4488</b> that, responsive to the registered at least one of a location and orientation of the motorcycle generates an augmentation element <b>44177</b> for presenting in the display <b>44175</b>, the generating based at least in part on the determined at least one augmentation parameter <b>44156</b>.

0310In embodiments, the motorcycle <b>44169</b> comprises a system for automating at least one control parameter of the motorcycle. In embodiments, the motorcycle <b>44169</b> is at least a semi-autonomous motorcycle. In embodiments, the motorcycle <b>44169</b> is automatically routed. In embodiments, the motorcycle <b>44169</b> is a self-driving motorcycle. In embodiments, the content <b>44176</b> in the environment is content that is visible in a portion of a field of view of the rider <b>44172</b> wearing the helmet. In embodiments, the machine learning on the input of the rider determines an emotional state of the rider and a value for the at least one parameter is adapted responsive to the rider emotional state. In embodiments, the machine learning on the input of the motorcycle determines an operational state of the motorcycle and a value for the at least one parameter is adapted responsive to the motorcycle operational state.

0311In embodiments, the helmet further comprises a motorcycle configuration expert system <b>44139</b> for recommending an adjustment of a value of the at least one parameter <b>44156</b> to the augmented reality system <b>4488</b> responsive to the at least one input.

0312In embodiments, leveraging network technologies for a transportation system may support a cognitive collective charging or refueling plan for vehicles in the transportation system. Such a transportation system may include an artificial intelligence system for taking inputs relating to a plurality of vehicles, such as self-driving vehicles, and determining at least one parameter of a re-charging or refueling plan for at least one of the plurality of vehicles based on the inputs.

0313In embodiments, the transportation system may be a vehicle transportation system. Such a vehicle transportation system may include a network-enabled vehicle information ingestion port <b>4532</b> that may provide a network (e.g., Internet and the like) interface through which inputs, such as inputs comprising operational state and energy consumption information from at least one of a plurality of network-enabled vehicles <b>4510</b> may be gathered. In embodiments, such inputs may be gathered in real time as the plurality of network-enabled vehicles <b>4510</b> connect to and deliver vehicle operational state, energy consumption and other related information. In embodiments, the inputs may relate to vehicle energy consumption and may be determined from a battery charge state of a portion of the plurality of vehicles. The inputs may include a route plan for the vehicle, an indicator of the value of charging of the vehicle, and the like. The inputs may include predicted traffic conditions for the plurality of vehicles. The transportation system may also include vehicle charging or refueling infrastructure that may include one or more vehicle charging infrastructure control system(s) <b>4534</b>. These control system(s) <b>4534</b> may receive the operational state and energy consumption information for the plurality of network-enabled vehicles <b>4510</b> via the ingestion port <b>4532</b> or directly through a common or set of connected networks, such as the Internet and the like. Such a transportation system may further include an artificial intelligence system <b>4536</b> that may be functionally connected with the vehicle charging infrastructure control system(s) <b>4534</b> that, for example, responsive to the receiving of the operational state and energy consumption information, may determine, provide, adjust or create at least one charging plan parameter <b>4514</b> upon which a charging plan <b>4512</b> for at least a portion of the plurality of network-enabled vehicles <b>4510</b> is dependent. This dependency may yield changes in the application of the charging plan <b>4512</b> by the control system(s) <b>4534</b>, such as when a processor of the control system(s) <b>4534</b> executes a program derived from or based on the charging plan <b>4512</b>. The charging infrastructure control system(s) <b>4534</b> may include a cloud-based computing system remote from charging infrastructure systems (e.g., remote from an electric vehicle charging kiosk and the like); it may also include a local charging infrastructure system <b>4538</b> that may be disposed with and/or integrated with an infrastructure element, such as a fuel station, a charging kiosk and the like. In embodiments, the artificial intelligence system <b>4536</b> may interface and coordinate with the cloud-based system <b>4534</b>, the local charging infrastructure system <b>4538</b> or both. In embodiments, coordination of the cloud-based system may take on a different form of interfacing, such as providing parameters that affect more than one charging kiosk and the like than may coordination with the local charging infrastructure system <b>4538</b>, which may provide information that the local system could use to adapt charging system control commands and the like that may be provided from, for example, a cloud-based control system <b>4534</b>. In an example, a cloud-based control system (that may control only a portion, such as a localized set, of available charging/refueling infrastructure devices) may respond to the charging plan parameter <b>4514</b> of the artificial intelligence system <b>4536</b> by setting a charging rate that facilitates highly parallel vehicle charging. However, the local charging infrastructure system <b>4538</b> may adapt this control plan, such as based on a control plan parameter provided to it by the artificial intelligence system <b>4536</b>, to permit a different charging rate (e.g., a faster charging rate), such as for a brief period to accommodate an accumulation of vehicles queued up or estimated to use a local charging kiosk in the period. In this way, an adjustment to the at least one parameter <b>4514</b> that when made to the charge infrastructure operation plan <b>4512</b> ensures that the at least one of the plurality of vehicles <b>4510</b> has access to energy renewal in a target energy renewal geographic region <b>4516</b>.

0314In embodiments, a charging or refueling plan may have a plurality of parameters that may impact a wide range of transportation aspects ranging from vehicle-specific to vehicle group-specific to vehicle location-specific and infrastructure impacting aspects. Therefore, a parameter of the plan may impact or relate to any of vehicle routing to charging infrastructure, amount of charge permitted to be provided, duration of time or rate for charging, battery conditions or state, battery charging profile, time required to charge to a minimum value that may be based on consumption needs of the vehicle(s), market value of charging, indicators of market value, market price, infrastructure provider profit, bids or offers for providing fuel or electricity to one or more charging or refueling infrastructure kiosks, available supply capacity, recharge demand (local, regional, system wide), and the like.

0315In embodiments, to facilitate a cognitive charging or refueling plan, the transportation system may include a recharging plan update facility that interacts with the artificial intelligence system <b>4536</b> to apply an adjustment value <b>4524</b> to the at least one of the plurality of recharging plan parameters <b>4514</b>. An adjustment value <b>4524</b> may be further adjusted based on feedback of applying the adjustment value. In embodiments, the feedback may be used by the artificial intelligence system <b>4534</b> to further adjust the adjustment value. In an example, feedback may impact the adjustment value applied to charging or refueling infrastructure facilities in a localized way, such as for a target recharging geographic region <b>4516</b> or geographic range relative to one or more vehicles. In embodiments, providing a parameter adjustment value may facilitate optimizing consumption of a remaining battery charge state of at least one of the plurality of vehicles.

0316By processing energy-related consumption, demand, availability, and access information and the like, the artificial intelligence system <b>4536</b> may optimize aspects of the transportation system, such as vehicle electricity usage as shown in the box at <b>4526</b>. The artificial intelligence system <b>4536</b> may further optimize at least one of recharging time, location, and amount. In an example, a recharging plan parameter that may be configured and updated based on feedback may be a routing parameter for the at least one of the plurality of vehicles as shown in the box at <b>4526</b>.

0317The artificial intelligence system <b>4536</b> may further optimize a transportation system charging or refueling control plan parameter <b>4514</b> to, for example, accommodate near-term charging needs for the plurality of rechargeable vehicles <b>4510</b> based on the optimized at least one parameter. The artificial intelligence system <b>4536</b> may execute an optimizing algorithm that may calculate energy parameters (including vehicle and non-vehicle energy), optimizes electricity usage for at least vehicles and/or charging or refueling infrastructure, and optimizes at least one charging or refueling infrastructure-specific recharging time, location, and amount.

0318In embodiments, the artificial intelligence system <b>4534</b> may predict a geolocation <b>4518</b> of one or more vehicles within a geographic region <b>4516</b>. The geographic region <b>4516</b> may include vehicles that are currently located in or predicted to be in the region and optionally may require or prefer recharging or refueling. As an example of predicting geolocation and its impact on a charging plan, a charging plan parameter may include allocation of vehicles currently in or predicted to be in the region to charging or refueling infrastructure in the geographic region <b>4516</b>. In embodiments, geolocation prediction may include receiving inputs relating to charging states of a plurality of vehicles within or predicted to be within a geolocation range so that the artificial intelligence system can optimize at least one charging plan parameter <b>4514</b> based on a prediction of geolocations of the plurality of vehicles.

0319There are many aspects of a charging plan that may be impacted. Some aspects may be financial related, such as automated negotiation of at least one of a duration, a quantity and a price for charging or refueling a vehicle.

0320The transportation system cognitive charging plan system may include the artificial intelligence system being configured with a hybrid neural network. A first neural network <b>4522</b> of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles (directly received from the vehicles or through the vehicle information port <b>4532</b>) and a second neural network <b>4520</b> of the hybrid neural network is used to process inputs relating to charging or refueling infrastructure and the like. In embodiments, the first neural network <b>4522</b> may process inputs comprising vehicle route and stored energy state information for a plurality of vehicles to predict for at least one of the plurality of vehicles a target energy renewal region. The second neural network <b>4520</b> may process vehicle energy renewal infrastructure usage and demand information for vehicle energy renewal infrastructure facilities within the target energy renewal region to determine at least one parameter <b>4514</b> of a charge infrastructure operational plan <b>4512</b> that facilitates access by the at least one of the plurality vehicles to renewal energy in the target energy renewal region <b>4516</b>. In embodiments, the first and/or second neural networks may be configured as any of the neural networks described herein including without limitation convolutional type networks.

0321In embodiments, a transportation system may be distributed and may include an artificial intelligence system <b>4536</b> for taking inputs relating to a plurality of vehicles <b>4510</b> and determining at least one parameter <b>4514</b> of a re-charging and refueling plan <b>4512</b> for at least one of the plurality of vehicles based on the inputs. In embodiments, such inputs may be gathered in real time as plurality of vehicles <b>4510</b> connect to and deliver vehicle operational state, energy consumption and other related information. In embodiments, the inputs may relate to vehicle energy consumption and may be determined from a battery charge state of a portion of the plurality of vehicles. The inputs may include a route plan for the vehicle, an indicator of the value of charging of the vehicle, and the like. The inputs may include predicted traffic conditions for the plurality of vehicles. The distributed transportation system may also include cloud-based and vehicle-based systems that exchange information about the vehicle, such as energy consumption and operational information and information about the transportation system, such as recharging or refueling infrastructure. The artificial intelligence system may respond to transportation system and vehicle information shared by the cloud and vehicle-based system with control parameters that facilitate executing a cognitive charging plan for at least a portion of charging or refueling infrastructure of the transportation system. The artificial intelligence system <b>4536</b> may determine, provide, adjust or create at least one charging plan parameter <b>4514</b> upon which a charging plan <b>4512</b> for at least a portion of the plurality of vehicles <b>4510</b> is dependent. This dependency may yield changes in the execution of the charging plan <b>4512</b> by at least one the cloud-based and vehicle-based systems, such as when a processor executes a program derived from or based on the charging plan <b>4512</b>.

0322In embodiments, an artificial intelligence system of a transportation system may facilitate execution of a cognitive charging plan by applying a vehicle recharging facility utilization optimization algorithm to a plurality of rechargeable vehicle-specific inputs, e.g., current operating state data for rechargeable vehicles present in a target recharging range of one of the plurality of rechargeable vehicles. The artificial intelligence system may also evaluate an impact of a plurality of recharging plan parameters on recharging infrastructure of the transportation system in the target recharging range. The artificial intelligence system may select at least one of the plurality of recharging plan parameters that facilitates, for example optimizing energy usage by the plurality of rechargeable vehicles and generate an adjustment value for the at least one of the plurality of recharging plan parameters. The artificial intelligence system may further predict a near-term need for recharging for a portion of the plurality of rechargeable vehicles within the target region based on, for example, operational status of the plurality of rechargeable vehicles that may be determined from the rechargeable vehicle-specific inputs. Based on this prediction and near-term recharging infrastructure availability and capacity information, the artificial intelligence system may optimize at least one parameter of the recharging plan. In embodiments, the artificial intelligence system may operate a hybrid neural network for the predicting and parameter selection or adjustment. In an example, a first portion of the hybrid neural network may process inputs that relates to route plans for one more rechargeable vehicles. In the example, a second portion of the hybrid neural network that is distinct from the first portion may process inputs relating to recharging infrastructure within a recharging range of at least one of the rechargeable vehicles. In this example, the second distinct portion of the hybrid neural net predicts the geolocation of a plurality of vehicles within the target region. To facilitate execution of the recharging plan, the parameter may impact an allocation of vehicles to at least a portion of recharging infrastructure within the predicted geographic region.

0323In embodiments, vehicles described herein may comprise a system for automating at least one control parameter of the vehicle. The vehicles may further at least operate as a semi-autonomous vehicle. The vehicles may be automatically routed. Also, the vehicles, recharging and otherwise may be self-driving vehicles.

0324In embodiments, leveraging network technologies for a transportation system may support a cognitive collective charging or refueling plan for vehicles in the transportation system. Such a transportation system may include an artificial intelligence system for taking inputs relating to battery status of a plurality of vehicles, such as self-driving vehicles and determining at least one parameter of a re-charging and/or refueling plan for optimizing battery operation of at least one of the plurality of vehicles based on the inputs.

0325In embodiments, such a vehicle transportation system may include a network-enabled vehicle information ingestion port <b>4632</b> that may provide a network (e.g., Internet and the like) interface through which inputs, such as inputs comprising operational state and energy consumption information and battery state from at least one of a plurality of network-enabled vehicles <b>4610</b> may be gathered. In embodiments, such inputs may be gathered in real time as a plurality of vehicles <b>4610</b> connect to a network and deliver vehicle operational state, energy consumption, battery state and other related information. In embodiments, the inputs may relate to vehicle energy consumption and may include a battery charge state of a portion of the plurality of vehicles. The inputs may include a route plan for the vehicle, an indicator of the value of charging of the vehicle, and the like. The inputs may include predicted traffic conditions for the plurality of vehicles. The transportation system may also include vehicle charging or refueling infrastructure that may include one or more vehicle charging infrastructure control systems <b>4634</b>. These control systems may receive the battery status information and the like for the plurality of network-enabled vehicles <b>4610</b> via the ingestion port <b>4632</b> and/or directly through a common or set of connected networks, such as an Internet infrastructure including wireless networks and the like. Such a transportation system may further include an artificial intelligence system <b>4636</b> that may be functionally connected with the vehicle charging infrastructure control systems that may, based on at least the battery status information from the portion of the plurality of vehicles determine, provide, adjust or create at least one charging plan parameter <b>4614</b> upon which a charging plan <b>4612</b> for at least a portion of the plurality of network-enabled vehicles <b>4610</b> is dependent. This parameter dependency may yield changes in the application of the charging plan <b>4612</b> by the control system(s) <b>4634</b>, such as when a processor of the control system(s) <b>4634</b> executes a program derived from or based on the charging plan <b>4612</b>. These changes may be applied to optimize anticipated battery usage of one or more of the vehicles. The optimizing may be vehicle-specific, aggregated across a set of vehicles, and the like. The charging infrastructure control system(s) <b>4634</b> may include a cloud-based computing system remote from charging infrastructure systems (e.g., remote from an electric vehicle charging kiosk and the like); it may also include a local charging infrastructure system <b>4638</b> that may be disposed with and/or integrated into an infrastructure element, such as a fuel station, a charging kiosk and the like. In embodiments, the artificial intelligence system <b>4636</b> may interface with the cloud-based system <b>4634</b>, the local charging infrastructure system <b>4638</b> or both. In embodiments, the artificial intelligence system may interface with individual vehicles to facilitate optimizing anticipated battery usage. In embodiments, interfacing with the cloud-based system may affect infrastructure-wide impact of a charging plan, such as providing parameters that affect more than one charging kiosk. Interfacing with the local charging infrastructure system <b>4638</b> may include providing information that the local system could use to adapt charging system control commands and the like that may be provided from, for example, a regional or broader control system, such as a cloud-based control system <b>4634</b>. In an example, a cloud-based control system (that may control only a target or geographic region, such as a localized set, a town, a county, a city, a ward, county and the like of available charging or refueling infrastructure devices) may respond to the charging plan parameter <b>4614</b> of the artificial intelligence system <b>4636</b> by setting a charging rate that facilitates highly parallel vehicle charging so that vehicle battery usage can be optimized. However, the local charging infrastructure system <b>4638</b> may adapt this control plan, such as based on a control plan parameter provided to it by the artificial intelligence system <b>4636</b>, to permit a different charging rate (e.g., a faster charging rate), such as for a brief period to accommodate an accumulation of vehicles for which anticipated battery usage is not yet optimized. In this way, an adjustment to the at least one parameter <b>4614</b> that when made to the charge infrastructure operation plan <b>4612</b> ensures that the at least one of the plurality of vehicles <b>4610</b> has access to energy renewal in a target energy renewal region <b>4616</b>. In embodiments, a target energy renewal region may be defined by a geofence that may be configured by an administrator of the region. In an example an administrator may have control or responsibility for a jurisdiction (e.g., a township, and the like). In the example, the administrator may configure a geofence for a region that is substantially congruent with the jurisdiction.

0326In embodiments, a charging or refueling plan may have a plurality of parameters that may impact a wide range of transportation aspects ranging from vehicle-specific to vehicle group-specific to vehicle location-specific and infrastructure impacting aspects. Therefore, a parameter of the plan may impact or relate to any of vehicle routing to charging infrastructure, amount of charge permitted to be provided, duration of time or rate for charging, battery conditions or state, battery charging profile, time required to charge to a minimum value that may be based on consumption needs of the vehicle(s), market value of charging, indicators of market value, market price, infrastructure provider profit, bids or offers for providing fuel or electricity to one or more charging or refueling infrastructure kiosks, available supply capacity, recharge demand (local, regional, system wide), maximum energy usage rate, time between battery charging, and the like.

0327In embodiments, to facilitate a cognitive charging or refueling plan, the transportation system may include a recharging plan update facility that interacts with the artificial intelligence system <b>4636</b> to apply an adjustment value <b>4624</b> to the at least one of the plurality of recharging plan parameters <b>4614</b>. An adjustment value <b>4624</b> may be further adjusted based on feedback of applying the adjustment value. In embodiments, the feedback may be used by the artificial intelligence system <b>4634</b> to further adjust the adjustment value. In an example, feedback may impact the adjustment value applied to charging or refueling infrastructure facilities in a localized way, such as impacting only a set of vehicles that are impacted by or projected to be impacted by a traffic jam so that their battery operation is optimized, so as to, for example, ensure that they have sufficient battery power throughout the duration of the traffic jam. In embodiments, providing a parameter adjustment value may facilitate optimizing consumption of a remaining battery charge state of at least one of the plurality of vehicles.

0328By processing energy-related consumption, demand, availability, and access information and the like, the artificial intelligence system <b>4636</b> may optimize aspects of the transportation system, such as vehicle electricity usage as shown in the box at <b>4626</b>. The artificial intelligence system <b>4636</b> may further optimize at least one of recharging time, location, and amount as shown in the box at <b>4626</b>. In an example a recharging plan parameter that may be configured and updated based on feedback may be a routing parameter for the at least one of the plurality of vehicles.

0329The artificial intelligence system <b>4636</b> may further optimize a transportation system charging or refueling control plan parameter <b>4614</b> to, for example accommodate near-term charging needs for the plurality of rechargeable vehicles <b>4610</b> based on the optimized at least one parameter. The artificial intelligence system <b>4636</b> may execute a vehicle recharging optimizing algorithm that may calculate energy parameters (including vehicle and non-vehicle energy) that may impact an anticipated battery usage, optimizes electricity usage for at least vehicles and/or charging or refueling infrastructure, and optimizes at least one charging or refueling infrastructure-specific recharging time, location, and amount.

0330In embodiments, the artificial intelligence system <b>4634</b> may predict a geolocation <b>4618</b> of one or more vehicles within a geographic region <b>4616</b>. The geographic region <b>4616</b> may include vehicles that are currently located in or predicted to be in the region and optionally may require or prefer recharging or refueling. As an example of predicting geolocation and its impact on a charging plan, a charging plan parameter may include allocation of vehicles currently in or predicted to be in the region to charging or refueling infrastructure in the geographic region <b>4616</b>. In embodiments, geolocation prediction may include receiving inputs relating to battery and battery charging states and recharging needs of a plurality of vehicles within or predicted to be within a geolocation range so that the artificial intelligence system can optimize at least one charging plan parameter <b>4614</b> based on a prediction of geolocations of the plurality of vehicles.

0331There are many aspects of a charging plan that may be impacted. Some aspects may be financial related, such as automated negotiation of at least one of a duration, a quantity and a price for charging or refueling a vehicle.

0332The transportation system cognitive charging plan system may include the artificial intelligence system being configured with a hybrid neural network. A first neural network <b>4622</b> of the hybrid neural network may be used to process inputs relating to battery charge or fuel states of the plurality of vehicles (directly received from the vehicles or through the vehicle information port <b>4632</b>) and a second neural network <b>4620</b> of the hybrid neural network is used to process inputs relating to charging or refueling infrastructure and the like. In embodiments, the first neural network <b>4622</b> may process inputs comprising information about a charging system of the vehicle and vehicle route and stored energy state information for a plurality of vehicles to predict for at least one of the plurality of vehicles a target energy renewal region. The second neural network <b>4620</b> may further predict a geolocation of a portion of the plurality of vehicles relative to another vehicle or set of vehicles. The second neural network <b>4620</b> may process vehicle energy renewal infrastructure usage and demand information for vehicle energy renewal infrastructure facilities within the target energy renewal region to determine at least one parameter <b>4614</b> of a charge infrastructure operational plan <b>4612</b> that facilitates access by the at least one of the plurality vehicles to renewal energy in the target energy renewal region <b>4616</b>. In embodiments, the first and/or second neural networks may be configured as any of the neural networks described herein including without limitation convolutional type networks.

0333In embodiments, a transportation system may be distributed and may include an artificial intelligence system <b>4636</b> for taking inputs relating to a plurality of vehicles <b>4610</b> and determining at least one parameter <b>4614</b> of a re-charging and refueling plan <b>4612</b> for at least one of the plurality of vehicles based on the inputs. In embodiments, such inputs may be gathered in real time as plurality of vehicles <b>4610</b> connect to a network and deliver vehicle operational state, energy consumption and other related information. In embodiments, the inputs may relate to vehicle energy consumption and may be determined from a battery charge state of a portion of the plurality of vehicles. The inputs may include a route plan for the vehicle, an indicator of the value of charging of the vehicle, and the like. The inputs may include predicted traffic conditions for the plurality of vehicles. The distributed transportation system may also include cloud-based and vehicle-based systems that exchange information about the vehicle, such as energy consumption and operational information and information about the transportation system, such as recharging or refueling infrastructure. The artificial intelligence system may respond to transportation system and vehicle information shared by the cloud and vehicle-based system with control parameters that facilitate executing a cognitive charging plan for at least a portion of charging or refueling infrastructure of the transportation system. The artificial intelligence system <b>4636</b> may determine, provide, adjust or create at least one charging plan parameter <b>4614</b> upon which a charging plan <b>4612</b> for at least a portion of the plurality of vehicles <b>4610</b> is dependent. This dependency may yield changes in the execution of the charging plan <b>4612</b> by at least one the cloud-based and vehicle-based systems, such as when a processor executes a program derived from or based on the charging plan <b>4612</b>.

0334In embodiments, an artificial intelligence system of a transportation system may facilitate execution of a cognitive charging plan by applying a vehicle recharging facility utilization of a vehicle battery operation optimization algorithm to a plurality of rechargeable vehicle-specific inputs, e.g., current operating state data for rechargeable vehicles present in a target recharging range of one of the plurality of rechargeable vehicles. The artificial intelligence system may also evaluate an impact of a plurality of recharging plan parameters on recharging infrastructure of the transportation system in the target recharging range. The artificial intelligence system may select at least one of the plurality of recharging plan parameters that facilitates, for example optimizing energy usage by the plurality of rechargeable vehicles and generate an adjustment value for the at least one of the plurality of recharging plan parameters. The artificial intelligence system may further predict a near-term need for recharging for a portion of the plurality of rechargeable vehicles within the target region based on, for example, operational status of the plurality of rechargeable vehicles that may be determined from the rechargeable vehicle-specific inputs. Based on this prediction and near-term recharging infrastructure availability and capacity information, the artificial intelligence system may optimize at least one parameter of the recharging plan. In embodiments, the artificial intelligence system may operate a hybrid neural network for the predicting and parameter selection or adjustment. In an example, a first portion of the hybrid neural network may process inputs that relates to route plans for one more rechargeable vehicles. In the example, a second portion of the hybrid neural network that is distinct from the first portion may process inputs relating to recharging infrastructure within a recharging range of at least one of the rechargeable vehicles. In this example, the second distinct portion of the hybrid neural net predicts the geolocation of a plurality of vehicles within the target region. To facilitate execution of the recharging plan, the parameter may impact an allocation of vehicles to at least a portion of recharging infrastructure within the predicted geographic region.

0335In embodiments, vehicles described herein may comprise a system for automating at least one control parameter of the vehicle. The vehicles may further at least operate as a semi-autonomous vehicle. The vehicles may be automatically routed. Also, the vehicles, recharging and otherwise may be self-driving vehicles.

0336In embodiments, leveraging network technologies for a transportation system may support a cognitive collective charging or refueling plan for vehicles in the transportation system. Such a transportation system may include a cloud-based artificial intelligence system for taking inputs relating to a plurality of vehicles, such as self-driving vehicles and determining at least one parameter of a re-charging and/or refueling plan for at least one of the plurality of vehicles based on the inputs.

0337In embodiments, such a vehicle transportation system may include a cloud-enabled vehicle information ingestion port <b>4732</b> that may provide a network (e.g., Internet and the like) interface through which inputs, such as inputs comprising operational state and energy consumption information from at least one of a plurality of network-enabled vehicles <b>4710</b> may be gathered and provided into cloud resources, such as the cloud-based control and artificial intelligence systems described herein. In embodiments, such inputs may be gathered in real time as a plurality of vehicles <b>4710</b> connect to the cloud and deliver vehicle operational state, energy consumption and other related information through at least the port <b>4732</b>. In embodiments, the inputs may relate to vehicle energy consumption and may be determined from a battery charge state of a portion of the plurality of vehicles. The inputs may include a route plan for the vehicle, an indicator of the value of charging of the vehicle, and the like. The inputs may include predicted traffic conditions for the plurality of vehicles. The transportation system may also include vehicle charging or refueling infrastructure that may include one or more vehicle charging infrastructure cloud-based control system(s) <b>4734</b>. These cloud-based control system(s) <b>4734</b> may receive the operational state and energy consumption information for the plurality of network-enabled vehicles <b>4710</b> via the cloud-enabled ingestion port <b>4732</b> and/or directly through a common or set of connected networks, such as the Internet and the like. Such a transportation system may further include a cloud-based artificial intelligence system <b>4736</b> that may be functionally connected with the vehicle charging infrastructure cloud-based control system(s) <b>4734</b> that, for example may determine, provide, adjust or create at least one charging plan parameter <b>4714</b> upon which a charging plan <b>4712</b> for at least a portion of the plurality of network-enabled vehicles <b>4710</b> is dependent. This dependency may yield changes in the application of the charging plan <b>4712</b> by the cloud-based control system(s) <b>4734</b>, such as when a processor of the cloud-based control system(s) <b>4734</b> executes a program derived from or based on the charging plan <b>4712</b>. The charging infrastructure cloud-based control system(s) <b>4734</b> may include a cloud-based computing system remote from charging infrastructure systems (e.g., remote from an electric vehicle charging kiosk and the like); it may also include a local charging infrastructure system <b>4738</b> that may be disposed with and/or integrated into an infrastructure element, such as a fuel station, a charging kiosk and the like. In embodiments, the cloud-based artificial intelligence system <b>4736</b> may interface and coordinate with the cloud-based charging infrastructure control system <b>4734</b>, the local charging infrastructure system <b>4738</b> or both. In embodiments, coordination of the cloud-based system may take on a form of interfacing, such as providing parameters that affect more than one charging kiosk and the like than may be different from coordination with the local charging infrastructure system <b>4738</b>, which may provide information that the local system could use to adapt cloud-based charging system control commands and the like that may be provided from, for example, a cloud-based control system <b>4734</b>. In an example, a cloud-based control system (that may control only a portion, such as a localized set, of available charging or refueling infrastructure devices) may respond to the charging plan parameter <b>4714</b> of the cloud-based artificial intelligence system <b>4736</b> by setting a charging rate that facilitates highly parallel vehicle charging. However, the local charging infrastructure system <b>4738</b> may adapt this control plan, such as based on a control plan parameter provided to it by the cloud-based artificial intelligence system <b>4736</b>, to permit a different charging rate (e.g., a faster charging rate), such as for a brief period to accommodate an accumulation of vehicles queued up or estimated to use a local charging kiosk in the period. In this way, an adjustment to the at least one parameter <b>4714</b> that when made to the charge infrastructure operation plan <b>4712</b> ensures that the at least one of the plurality of vehicles <b>4710</b> has access to energy renewal in a target energy renewal region <b>4716</b>.

0338In embodiments, a charging or refueling plan may have a plurality of parameters that may impact a wide range of transportation aspects ranging from vehicle-specific to vehicle group-specific to vehicle location-specific and infrastructure impacting aspects. Therefore, a parameter of the plan may impact or relate to any of vehicle routing to charging infrastructure, amount of charge permitted to be provided, duration of time or rate for charging, battery conditions or state, battery charging profile, time required to charge to a minimum value that may be based on consumption needs of the vehicle(s), market value of charging, indicators of market value, market price, infrastructure provider profit, bids or offers for providing fuel or electricity to one or more charging or refueling infrastructure kiosks, available supply capacity, recharge demand (local, regional, system wide), and the like.

0339In embodiments, to facilitate a cognitive charging or refueling plan, the transportation system may include a recharging plan update facility that interacts with the cloud-based artificial intelligence system <b>4736</b> to apply an adjustment value <b>4724</b> to the at least one of the plurality of recharging plan parameters <b>4714</b>. An adjustment value <b>4724</b> may be further adjusted based on feedback of applying the adjustment value. In embodiments, the feedback may be used by the cloud-based artificial intelligence system <b>4734</b> to further adjust the adjustment value. In an example, feedback may impact the adjustment value applied to charging or refueling infrastructure facilities in a localized way, such as for a target recharging area <b>4716</b> or geographic range relative to one or more vehicles. In embodiments, providing a parameter adjustment value may facilitate optimizing consumption of a remaining battery charge state of at least one of the plurality of vehicles.

0340By processing energy-related consumption, demand, availability, and access information and the like, the cloud-based artificial intelligence system <b>4736</b> may optimize aspects of the transportation system, such as vehicle electricity usage. The cloud-based artificial intelligence system <b>4736</b> may further optimize at least one of recharging time, location, and amount. In an example, a recharging plan parameter that may be configured and updated based on feedback may be a routing parameter for the at least one of the plurality of vehicles.

0341The cloud-based artificial intelligence system <b>4736</b> may further optimize a transportation system charging or refueling control plan parameter <b>4714</b> to, for example, accommodate near-term charging needs for the plurality of rechargeable vehicles <b>4710</b> based on the optimized at least one parameter. The cloud-based artificial intelligence system <b>4736</b> may execute an optimizing algorithm that may calculate energy parameters (including vehicle and non-vehicle energy), optimizes electricity usage for at least vehicles and/or charging or refueling infrastructure, and optimizes at least one charging or refueling infrastructure-specific recharging time, location, and amount.

0342In embodiments, the cloud-based artificial intelligence system <b>4734</b> may predict a geolocation <b>4718</b> of one or more vehicles within a geographic region <b>4716</b>. The geographic region <b>4716</b> may include vehicles that are currently located in or predicted to be in the region and optionally may require or prefer recharging or refueling. As an example of predicting geolocation and its impact on a charging plan, a charging plan parameter may include allocation of vehicles currently in or predicted to be in the region to charging or refueling infrastructure in the geographic region <b>4716</b>. In embodiments, geolocation prediction may include receiving inputs relating to charging states of a plurality of vehicles within or predicted to be within a geolocation range so that the cloud-based artificial intelligence system can optimize at least one charging plan parameter <b>4714</b> based on a prediction of geolocations of the plurality of vehicles.

0343There are many aspects of a charging plan that may be impacted. Some aspects may be financial related, such as automated negotiation of at least one of a duration, a quantity and a price for charging or refueling a vehicle.

0344The transportation system cognitive charging plan system may include the cloud-based artificial intelligence system being configured with a hybrid neural network. A first neural network <b>4722</b> of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles (directly received from the vehicles or through the vehicle information port <b>4732</b>) and a second neural network <b>4720</b> of the hybrid neural network is used to process inputs relating to charging or refueling infrastructure and the like. In embodiments, the first neural network <b>4722</b> may process inputs comprising vehicle route and stored energy state information for a plurality of vehicles to predict for at least one of the plurality of vehicles a target energy renewal region. The second neural network <b>4720</b> may process vehicle energy renewal infrastructure usage and demand information for vehicle energy renewal infrastructure facilities within the target energy renewal region to determine at least one parameter <b>4714</b> of a charge infrastructure operational plan <b>4712</b> that facilitates access by the at least one of the plurality vehicles to renewal energy in the target energy renewal region <b>4716</b>. In embodiments, the first and/or second neural networks may be configured as any of the neural networks described herein including without limitation convolutional type networks.

0345In embodiments, a transportation system may be distributed and may include a cloud-based artificial intelligence system <b>4736</b> for taking inputs relating to a plurality of vehicles <b>4710</b> and determining at least one parameter <b>4714</b> of a re-charging and refueling plan <b>4712</b> for at least one of the plurality of vehicles based on the inputs. In embodiments, such inputs may be gathered in real time as plurality of vehicles <b>4710</b> connect to and deliver vehicle operational state, energy consumption and other related information. In embodiments, the inputs may relate to vehicle energy consumption and may be determined from a battery charge state of a portion of the plurality of vehicles. The inputs may include a route plan for the vehicle, an indicator of the value of charging of the vehicle, and the like. The inputs may include predicted traffic conditions for the plurality of vehicles. The distributed transportation system may also include cloud-based and vehicle-based systems that exchange information about the vehicle, such as energy consumption and operational information and information about the transportation system, such as recharging or refueling infrastructure. The cloud-based artificial intelligence system may respond to transportation system and vehicle information shared by the cloud and vehicle-based system with control parameters that facilitate executing a cognitive charging plan for at least a portion of charging or refueling infrastructure of the transportation system. The cloud-based artificial intelligence system <b>4736</b> may determine, provide, adjust or create at least one charging plan parameter <b>4714</b> upon which a charging plan <b>4712</b> for at least a portion of the plurality of vehicles <b>4710</b> is dependent. This dependency may yield changes in the execution of the charging plan <b>4712</b> by at least one the cloud-based and vehicle-based systems, such as when a processor executes a program derived from or based on the charging plan <b>4712</b>.

0346In embodiments, a cloud-based artificial intelligence system of a transportation system may facilitate execution of a cognitive charging plan by applying a vehicle recharging facility utilization optimization algorithm to a plurality of rechargeable vehicle-specific inputs, e.g., current operating state data for rechargeable vehicles present in a target recharging range of one of the plurality of rechargeable vehicles. The cloud-based artificial intelligence system may also evaluate an impact of a plurality of recharging plan parameters on recharging infrastructure of the transportation system in the target recharging range. The cloud-based artificial intelligence system may select at least one of the plurality of recharging plan parameters that facilitates, for example optimizing energy usage by the plurality of rechargeable vehicles and generate an adjustment value for the at least one of the plurality of recharging plan parameters. The cloud-based artificial intelligence system may further predict a near-term need for recharging for a portion of the plurality of rechargeable vehicles within the target region based on, for example operational status of the plurality of rechargeable vehicles that may be determined from the rechargeable vehicle-specific inputs. Based on this prediction and near-term recharging infrastructure availability and capacity information, the cloud-based artificial intelligence system may optimize at least one parameter of the recharging plan. In embodiments, the cloud-based artificial intelligence system may operate a hybrid neural network for the predicting and parameter selection or adjustment. In an example, a first portion of the hybrid neural network may process inputs that relates to route plans for one more rechargeable vehicles. In the example, a second portion of the hybrid neural network that is distinct from the first portion may process inputs relating to recharging infrastructure within a recharging range of at least one of the rechargeable vehicles. In this example, the second distinct portion of the hybrid neural net predicts the geolocation of a plurality of vehicles within the target region. To facilitate execution of the recharging plan, the parameter may impact an allocation of vehicles to at least a portion of recharging infrastructure within the predicted geographic region.

0347In embodiments, vehicles described herein may comprise a system for automating at least one control parameter of the vehicle. The vehicles may further at least operate as a semi-autonomous vehicle. The vehicles may be automatically routed. Also, the vehicles, recharging and otherwise may be self-driving vehicles.

0348Referring to <figref idref="DRAWINGS">FIG. <b>48</b></figref>, provided herein are transportation systems <b>4811</b> having a robotic process automation system <b>48181</b> (RPA system). In embodiments, data is captured for each of a set of individuals/users <b>4891</b> as the individuals/users <b>4890</b> interact with a user interface <b>4823</b> of a vehicle <b>4811</b>, and an artificial intelligence system <b>4836</b> is trained using the data and interacts with the vehicle <b>4810</b> to automatically undertake actions with the vehicle <b>4810</b> on behalf of the user <b>4890</b>. Data <b>48114</b> collected for the RPA system <b>48181</b> may include a sequence of images, sensor data, telemetry data, or the like, among many other types of data described throughout this disclosure. Interactions of an individual/user <b>4890</b> with a vehicle <b>4810</b> may include interactions with various vehicle interfaces as described throughout this disclosure. For example, a robotic process automation (RPA) system <b>4810</b> may observe patterns of a driver, such as braking patterns, typical following distance behind other vehicles, approach to curves (e.g., entry angle, entry speed, exit angle, exit speed and the like), acceleration patterns, lane preferences, passing preferences, and the like. Such patterns may be obtained through vision systems <b>48186</b> (e.g., ones observing the driver, the steering wheel, the brake, the surrounding environment <b>48171</b>, and the like), through vehicle data systems <b>48185</b> (e.g., data streams indicating states and changes in state in steering, braking and the like, as well as forward and rear-facing cameras and sensors), through connected systems <b>48187</b> (e.g., GPS, cellular systems, and other network systems, as well as peer-to-peer, vehicle-to-vehicle, mesh and cognitive networks, among others), and other sources. Using a training data set, the RPA system <b>48181</b>, such as via a neural network <b>48108</b> of any of the types described herein, may learn to drive in the same style as a driver. In embodiments, the RPA system <b>48181</b> may learn changes in style, such as varying levels of aggressiveness in different situations, such as based on time of day, length of trip, type of trip, or the like. Thus, a self-driving car may learn to drive like its typical driver. Similarly, an RPA system <b>48181</b> may be used to observe driver, passenger, or other individual interactions with a navigation system, an audio entertainment system, a video entertainment system, a climate control system, a seat warming and/or cooling system, a steering system, a braking system, a mirror system, a window system, a door system, a trunk system, a fueling system, a moonroof system, a ventilation system, a lumbar support system, a seat positioning system, a GPS system, a WIFI system, a glovebox system, or other system.

0349An aspect provided herein includes a system <b>4811</b> for transportation, comprising: a robotic process automation system <b>48181</b>. In embodiments, a set of data is captured for each user <b>4890</b> in a set of users <b>4891</b> as each user <b>4890</b> interacts with a user interface <b>4823</b> of a vehicle <b>4810</b>. In embodiments, an artificial intelligence system <b>4836</b> is trained using the set of data <b>48114</b> to interact with the vehicle <b>4810</b> to automatically undertake actions with the vehicle <b>4810</b> on behalf of the user <b>4890</b>.

0350<figref idref="DRAWINGS">FIG. <b>49</b></figref> illustrates a method <b>4900</b> of robotic process automation to facilitate mimicking human operator operation of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At <b>4902</b> the method includes tracking human interactions with a vehicle control-facilitating interface. At <b>4904</b> the method includes recording the tracked human interactions in a robotic process automation system training data structure. At <b>4906</b> the method includes tracking vehicle operational state information of the vehicle. In embodiments, the vehicle is to be controlled through the vehicle control-facilitating interface. At <b>4908</b> the method includes recording the vehicle operational state information in the robotic process automation system training data structure. At <b>4909</b> the method includes training, through the use of at least one neural network, an artificial intelligence system to operate the vehicle in a manner consistent with the human interactions based on the human interactions and the vehicle operational state information in the robotic process automation system training data structure.

0351In embodiments, the method further comprises controlling at least one aspect of the vehicle with the trained artificial intelligence system. In embodiments, the method further comprises applying deep learning to the controlling the at least one aspect of the vehicle by structured variation in the controlling the at least one aspect of the vehicle to mimic the human interactions and processing feedback from the controlling the at least one aspect of the vehicle with machine learning. In embodiments, the controlling at least one aspect of the vehicle is performed via the vehicle control-facilitating interface.

0352In embodiments, the controlling at least one aspect of the vehicle is performed by the artificial intelligence system emulating the control-facilitating interface being operated by the human. In embodiments, the vehicle control-facilitating interface comprises at least one of an audio capture system to capture audible expressions of the human, a human-machine interface, a mechanical interface, an optical interface and a sensor-based interface. In embodiments, the tracking vehicle operational state information comprises tracking at least one of a set of vehicle systems and a set of vehicle operational processes affected by the human interactions. In embodiments, the tracking vehicle operational state information comprises tracking at least one vehicle system element. In embodiments, the at least one vehicle system element is controlled via the vehicle control-facilitating interface. In embodiments, the at least one vehicle system element is affected by the human interactions. In embodiments, the tracking vehicle operational state information comprises tracking the vehicle operational state information before, during, and after the human interaction.

0353In embodiments, the tracking vehicle operational state information comprises tracking at least one of a plurality of vehicle control system outputs that result from the human interactions and vehicle operational results achieved in response to the human interactions. In embodiments, the vehicle is to be controlled to achieve results that are consistent with results achieved via the human interactions. In embodiments, the method further comprises tracking and recording conditions proximal to the vehicle with a plurality of vehicle mounted sensors. In embodiments, the training of the artificial intelligence system is further responsive to the conditions proximal to the vehicle tracked contemporaneously to the human interactions. In embodiments, the training is further responsive to a plurality of data feeds from remote sensors, the plurality of data feeds comprising data collected by the remove sensors contemporaneous to the human interactions. In embodiments, the artificial intelligence system employs a workflow that involves decision-making and the robotic process automation system facilitates automation of the decision-making. In embodiments, the artificial intelligence system employs a workflow that involves remote control of the vehicle and the robotic process automation system facilitates automation of remotely controlling the vehicle.

0354An aspect provided herein includes a transportation system <b>4811</b> for mimicking human operation of a vehicle <b>4810</b>, comprising: a robotic process automation system <b>48181</b> comprising: an operator data collection module <b>48182</b> to capture human operator interaction with a vehicle control system interface <b>48191</b>; a vehicle data collection module <b>48183</b> to capture vehicle response and operating conditions associated at least contemporaneously with the human operator interaction; and an environment data collection module <b>48184</b> to capture instances of environmental information associated at least contemporaneously with the human operator interaction; and an artificial intelligence system <b>4836</b> to learn to mimic the human operator (e.g., user <b>4890</b>) to control the vehicle <b>4810</b> responsive to the robotic process automation system <b>48181</b> detecting data <b>48114</b> indicative of at least one of a plurality of the instances of environmental information associated with the contemporaneously captured vehicle response and operating conditions.

0355In embodiments, the operator data collection module <b>48182</b> is to capture patterns of data including braking patterns, follow-behind distance, approach to curve acceleration patterns, lane preferences, and passing preferences. In embodiments, vehicle data collection module <b>48183</b> captures data from a plurality of vehicle data systems <b>48185</b> that provide data streams indicating states and changes in state in steering, braking, acceleration, forward looking images, and rear-looking images. In embodiments, the artificial intelligence system <b>4836</b> includes a neural network <b>48108</b> for training the artificial intelligence system <b>4836</b>.

0356<figref idref="DRAWINGS">FIG. <b>50</b></figref> illustrates a robotic process automation method <b>5000</b> of mimicking human operation of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At <b>5002</b> the method includes capturing human operator interactions with a vehicle control system interface. At <b>5004</b> the method includes capturing vehicle response and operating conditions associated at least contemporaneously with the human operator interaction. At <b>5006</b> the method includes capturing instances of environmental information associated at least contemporaneously with the human operator interaction. At <b>5008</b> the method includes training an artificial intelligence system to control the vehicle mimicking the human operator responsive to the environment data collection module detecting data indicative of at least one of a plurality of the instances of environmental information associated with the contemporaneously captured vehicle response and operating conditions.

0357In embodiments, the method further comprises applying deep learning in the artificial intelligence system to optimize a margin of vehicle operating safety by affecting the controlling of the at least one aspect of the vehicle by structured variation in the controlling of the at least one aspect of the vehicle to mimic the human interactions and processing feedback from the controlling the at least one aspect of the vehicle with machine learning. In embodiments, the robotic process automation system facilitates automation of a decision-making workflow employed by the artificial intelligence system. In embodiments, the robotic process automation system facilitates automation of a remote control workflow that the artificial intelligence system employs to remotely control the vehicle.

0358Referring to <figref idref="DRAWINGS">FIG. <b>51</b></figref>, a transportation system <b>5111</b> is provided having an artificial intelligence system <b>5136</b> that automatically randomizes a parameter of an in-vehicle experience in order to improve a user state that benefits from variation. In embodiments, a system used to control a driver or passenger experience (such as in a self-driving car, assisted car, or conventional vehicle) may be configured to automatically undertake actions based on an objective or feedback function, such as where an artificial intelligence system <b>5136</b> is trained on outcomes from a training data set to provide outputs to one or more vehicle systems to improve health, satisfaction, mood, safety, one or more financial metrics, efficiency, or the like.

0359Such systems may involve a wide range of in-vehicle experience parameters (including any of the experience parameters described herein, such as driving experience (including assisted and self-driving, as well as vehicle responsiveness to inputs, such as in controlled suspension performance, approaches to curves, braking and the like), seat positioning (including lumbar support, leg room, seatback angle, seat height and angle, etc.), climate control (including ventilation, window or moonroof state (e.g., open or closed), temperature, humidity, fan speed, air motion and the like), sound (e.g., volume, bass, treble, individual speaker control, focus area of sound, etc.), content (audio, video and other types, including music, news, advertising and the like), route selection (e.g., for speed, for road experience (e.g., smooth or rough, flat or hilly, straight or curving), for points of interest (POIs), for view (e.g., scenic routes), for novelty (e.g., to see different locations), and/or for defined purposes (e.g., shopping opportunities, saving fuel, refueling opportunities, recharging opportunities, or the like).

0360In many situations, variation of one or more vehicle experience parameters may provide or result in a preferred state for a vehicle <b>5110</b> (or set of vehicles), a user (such as vehicle rider <b>51120</b>), or both, as compared to seeking to find a single optimized state of such a parameter. For example, while a user may have a preferred seat position, sitting in the same position every day, or during an extended period on the same day, may have adverse effects, such as placing undue pressure on certain joints, promoting atrophy of certain muscles, diminishing flexibility of soft tissue, or the like. In such a situation, an automated control system (including one that is configured to use artificial intelligence of any of the types described herein) may be configured to induce variation in one or more of the user experience parameters described herein, optionally with random variation or with variation that is according to a prescribed pattern, such as one that may be prescribed according to a regimen, such as one developed to provide physical therapy, chiropractic, or other medical or health benefits. As one example, seat positioning may be varied over time to promote health of joints, muscles, ligaments, cartilage or the like. As another example, consistent with evidence that human health is improved when an individual experiences significant variations in temperature, humidity, and other climate factors, a climate control system may be varied (randomly or according to a defined regimen) to provide varying temperature, humidity, fresh air (including by opening windows or ventilation) or the like in order to improve the health, mood, or alertness of a user.

0361An artificial intelligence-based control system <b>5136</b> may be trained on a set of outcomes (of various types described herein) to provide a level of variation of a user experience that achieves desired outcomes, including selection of the timing and extent of such variations. As another example, an audio system may be varied to preserve hearing (such as based on tracking accumulated sound pressure levels, accumulated dosage, or the like), to promote alertness (such as by varying the type of content), and/or to improve health (such as by providing a mix of stimulating and relaxing content). In embodiments, such an artificial intelligence system <b>5136</b> may be fed sensor data <b>51444</b>, such as from a wearable device <b>51157</b> (including a sensor set) or a physiological sensing system <b>51190</b>, which includes a set of systems and/or sensors capable of providing physiological monitoring within a vehicle <b>5110</b> (e.g., a vison-based system <b>51186</b> that observes a user, a sensor <b>5125</b> embedded in a seat, a steering wheel, or the like that can measure a physiological parameter, or the like). For example, a vehicle interface <b>51188</b> (such as a steering wheel or any other interface described herein) can measure a physiological parameter (e.g., galvanic skin response, such as to indicate a stress level, cortisol level, or the like of a driver or other user), which can be used to indicate a current state for purposes of control or can be used as part of a training data set to optimize one or more parameters that may benefit from control, including control of variation of user experience to achieve desired outcomes. In one such example, an artificial intelligence system <b>5136</b> may vary parameters, such as driving experience, music and the like, to account for changes in hormonal systems of the user (such as cortisol and other adrenal system hormones), such as to induce healthy changes in state (consistent with evidence that varying cortisol levels over the course of a day are typical in healthy individuals, but excessively high or low levels at certain times of day may be unhealthy or unsafe). Such a system may, for example, “amp up” the experience with more aggressive settings (e.g., more acceleration into curves, tighter suspension, and/or louder music) in the morning when rising cortisol levels are healthy and “mellow out” the experience (such as by softer suspension, relaxing music and/or gentle driving motion) in the afternoon when cortisol levels should be dropping to lower levels to promote health. Experiences may consider both health of the user and safety, such as by ensuring that levels vary over time, but are sufficiently high to assure alertness (and hence safety) in situations where high alertness is required. While cortisol (an important hormone) is provided as an example, user experience parameters may be controlled (optionally with random or configured variation) with respect to other hormonal or biological systems, including insulin-related systems, cardiovascular systems (e.g., relating to pulse and blood pressure), gastrointestinal systems, and many others.

0362An aspect provided herein includes a system for transportation <b>5111</b>, comprising: an artificial intelligence system <b>5136</b> to automatically randomize a parameter of an in-vehicle experience to improve a user state. In embodiments, the user state benefits from variation of the parameter.

0363An aspect provided herein includes a system for transportation <b>5111</b>, comprising: a vehicle interface <b>51188</b> for gathering physiological sensed data of a rider <b>51120</b> in the vehicle <b>5110</b>; and an artificial intelligence-based circuit <b>51189</b> that is trained on a set of outcomes related to rider in-vehicle experience and that induces, responsive to the sensed rider physiological data, variation in one or more of the user experience parameters to achieve at least one desired outcome in the set of outcomes, the inducing variation including control of timing and extent of the variation.

0364In embodiments, the induced variation includes random variation. In embodiments, the induced variation includes variation that is according to a prescribed pattern. In embodiments, the prescribed pattern is prescribed according to a regimen. In embodiments, the regimen is developed to provide at least one of physical therapy, chiropractic, and other medical health benefits. In embodiments, the one or more user experience parameters affect at least one of seat position, temperature, humidity, cabin air source, or audio output. In embodiments, the vehicle interface <b>51188</b> comprises at least one wearable sensor <b>51157</b> disposed to be worn by the rider <b>51120</b>. In embodiments, the vehicle interface <b>51188</b> comprises a vision system <b>51186</b> disposed to capture and analyze images from a plurality of perspectives of the rider <b>51120</b>. In embodiments, the variation in one or more of the user experience parameters comprises variation in control of the vehicle <b>5110</b>.

0365In embodiments, variation in control of the vehicle <b>5110</b> includes configuring the vehicle <b>5110</b> for aggressive driving performance. In embodiments, variation in control of the vehicle <b>5110</b> includes configuring the vehicle <b>5110</b> for non-aggressive driving performance. In embodiments, the variation is responsive to the physiological sensed data that includes an indication of a hormonal level of the rider <b>51120</b>, and the artificial intelligence-based circuit <b>51189</b> varies the one or more user experience parameters to promote a hormonal state that promotes rider safety.

0366Referring now to <figref idref="DRAWINGS">FIG. <b>52</b></figref>, also provided herein are transportation systems <b>5211</b> having a system <b>52192</b> for taking an indicator of a hormonal system level of a user <b>5290</b> and automatically varying a user experience in the vehicle <b>5210</b> to promote a hormonal state that promotes safety.

0367An aspect provided herein includes a system for transportation <b>5211</b>, comprising: a system <b>52192</b> for detecting an indicator of a hormonal system level of a user <b>5290</b> and automatically varying a user experience in a vehicle <b>5210</b> to promote a hormonal state that promotes safety.

0368An aspect provided herein includes a system for transportation <b>5211</b> comprising: a vehicle interface <b>52188</b> for gathering hormonal state data of a rider (e.g., user <b>5290</b>) in the vehicle <b>5210</b>; and an artificial intelligence-based circuit <b>52189</b> that is trained on a set of outcomes related to rider in-vehicle experience and that induces, responsive to the sensed rider hormonal state data, variation in one or more of the user experience parameters to achieve at least one desired outcome in the set of outcomes, the set of outcomes including a least one outcome that promotes rider safety, the inducing variation including control of timing and extent of the variation.

0369In embodiments, the variation in the one or more user experience parameters is controlled by the artificial intelligence system <b>5236</b> to promote a desired hormonal state of the rider (e.g., user <b>5290</b>). In embodiments, the desired hormonal state of the rider promotes safety. In embodiments, the at least one desired outcome in the set of outcomes is the at least one outcome that promotes rider safety. In embodiments, the variation in the one or more user experience parameters includes varying at least one of a food and a beverage offered to the rider (e.g., user <b>5290</b>). In embodiments, the one or more user experience parameters affect at least one of seat position, temperature, humidity, cabin air source, or audio output. In embodiments, the vehicle interface <b>52188</b> comprises at least one wearable sensor <b>52157</b> disposed to be worn by the rider (e.g., user <b>5290</b>).

0370In embodiments, the vehicle interface <b>52188</b> comprises a vision system <b>52186</b> disposed to capture and analyze images from a plurality of perspectives of the rider (e.g., user <b>5290</b>). In embodiments, the variation in one or more of the user experience parameters comprises variation in control of the vehicle <b>5210</b>. In embodiments, variation in control of the vehicle <b>5210</b> includes configuring the vehicle <b>5210</b> for aggressive driving performance. In embodiments, variation in control of the vehicle <b>5210</b> includes configuring the vehicle <b>5210</b> for non-aggressive driving performance.

0371Referring to <figref idref="DRAWINGS">FIG. <b>53</b></figref>, provided herein are transportation systems <b>5311</b> having a system for optimizing at least one of a vehicle parameter <b>53159</b> and a user experience parameter <b>53205</b> to provide a margin of safety <b>53204</b>. In embodiments, the margin of safety <b>53204</b> may be a user-selected margin of safety or user-based margin of safety, such as selected based on a profile of a user or actively selected by a user, such as by interaction with a user interface, or selected based on a profile developed by tracking user behavior, including behavior in a vehicle <b>5310</b> and in other contexts, such as on social media, in e-commerce, in consuming content, in moving from place-to-place, or the like. In many situations, there is a tradeoff between optimizing the performance of a dynamic system (such as to achieve some objective function, like fuel efficiency) and one or more risks that are present in the system. This is particularly true in situations where there is some asymmetry between the benefits of optimizing one or more parameters and the risks that are present in the dynamic systems in which the parameter plays a role. As an example, seeking to minimize travel time (such as for a daily commute), leads to an increased likelihood of arriving late, because a wide range of effects in dynamic systems, such as ones involving vehicle traffic, tend to cascade and periodically produce travel times that vary widely (and quite often adversely). Variances in many systems are not symmetrical; for example, unusually uncrowded roads may improve a 30-mile commute time by a few minutes, but an accident, or high congestion, can increase the same commute by an hour or more. Thus, to avoid risks that have high adverse consequences, a wide margin of safety may be required. In embodiments, systems are provided herein for using an expert system (which may be model-based, rule-based, deep learning, a hybrid, or other intelligent systems as described herein) to provide a desired margin of safety with respect to adverse events that are present in transportation-related dynamic systems. The margin of safety <b>53204</b> may be provided via an output of the expert system <b>5336</b>, such as an instruction, a control parameter for a vehicle <b>5310</b> or an in-vehicle user experience, or the like. An artificial intelligence system <b>5336</b> may be trained to provide the margin of safety <b>53204</b> based on a training set of data based on outcomes of transportation systems, such as traffic data, weather data, accident data, vehicle maintenance data, fueling and charging system data (including in-vehicle data and data from infrastructure systems, such as charging stations, fueling stations, and energy production, transportation, and storage systems), user behavior data, user health data, user satisfaction data, financial information (e.g., user financial information, pricing information (e.g., for fuel, for food, for accommodations along a route, and the like), vehicle safety data, failure mode data, vehicle information system data, and the like), and many other types of data as described herein and in the documents incorporated by reference herein.

0372An aspect provided herein includes a system for transportation <b>5311</b>, comprising: a system for optimizing at least one of a vehicle parameter <b>53159</b> and a user experience parameter <b>53205</b> to provide a margin of safety <b>53204</b>.

0373An aspect provided herein includes a transportation system <b>5311</b> for optimizing a margin of safety when mimicking human operation of a vehicle <b>5310</b>, the transportation system <b>5311</b> comprising: a set of robotic process automation systems <b>53181</b> comprising: an operator data collection module <b>53182</b> to capture human operator <b>5390</b> interactions <b>53201</b> with a vehicle control system interface <b>53191</b>; a vehicle data collection module <b>53183</b> to capture vehicle response and operating conditions associated at least contemporaneously with the human operator interaction <b>53201</b>; an environment data collection module <b>53184</b> to capture instances of environmental information <b>53203</b> associated at least contemporaneously with the human operator interactions <b>53201</b>; and an artificial intelligence system <b>5336</b> to learn to control the vehicle <b>5310</b> with an optimized margin of safety while mimicking the human operator. In embodiments, the artificial intelligence system <b>5336</b> is responsive to the robotic process automation system <b>53181</b>. In embodiments, the artificial intelligence system <b>5336</b> is to detect data indicative of at least one of a plurality of the instances of environmental information associated with the contemporaneously captured vehicle response and operating conditions. In embodiments, the optimized margin of safety is to be achieved by training the artificial intelligence system <b>5336</b> to control the vehicle <b>5310</b> based on a set of human operator interaction data collected from interactions of a set of expert human vehicle operators with the vehicle control system interface <b>53191</b>.

0374In embodiments, the operator data collection module <b>53182</b> captures patterns of data including braking patterns, follow-behind distance, approach to curve acceleration patterns, lane preferences, or passing preferences. In embodiments, the vehicle data collection module <b>53183</b> captures data from a plurality of vehicle data systems that provide data streams indicating states and changes in state in steering, braking, acceleration, forward looking images, or rear-looking images. In embodiments, the artificial intelligence system includes a neural network <b>53108</b> for training the artificial intelligence system <b>53114</b>.

0375<figref idref="DRAWINGS">FIG. <b>54</b></figref> illustrates a method <b>5400</b> of robotic process automation for achieving an optimized margin of vehicle operational safety in accordance with embodiments of the systems and methods disclosed herein. At <b>5402</b> the method includes tracking expert vehicle control human interactions with a vehicle control-facilitating interface. At <b>5404</b> the method includes recording the tracked expert vehicle control human interactions in a robotic process automation system training data structure. At <b>5406</b> the method includes tracking vehicle operational state information of a vehicle. At <b>5407</b> the method includes recording vehicle operational state information in the robotic process automation system training data structure. At <b>5408</b> the method includes training, via at least one neural network, the vehicle to operate with an optimized margin of vehicle operational safety in a manner consistent with the expert vehicle control human interactions based on the expert vehicle control human interactions and the vehicle operational state information in the robotic process automation system training data structure. At <b>5409</b> the method includes controlling at least one aspect of the vehicle with the trained artificial intelligence system.

0376Referring to <figref idref="DRAWINGS">FIG. <b>53</b></figref> and <figref idref="DRAWINGS">FIG. <b>54</b></figref>, in embodiments, the method further comprises applying deep learning to optimize the margin of vehicle operational safety by controlling the at least one aspect of the vehicle through structured variation in the controlling the at least one aspect of the vehicle to mimic the expert vehicle control human interactions <b>53201</b> and processing feedback from the controlling the at least one aspect of the vehicle with machine learning. In embodiments, the controlling at least one aspect of the vehicle is performed via the vehicle control-facilitating interface <b>53191</b>. In embodiments, the controlling at least one aspect of the vehicle is performed by the artificial intelligence system emulating the control-facilitating interface being operated by the expert vehicle control human <b>53202</b>. In embodiments, the vehicle control-facilitating interface <b>53191</b> comprises at least one of an audio capture system to capture audible expressions of the expert vehicle control human, a human-machine interface, mechanical interface, an optical interface and a sensor-based interface. In embodiments, the tracking vehicle operational state information comprises tracking at least one of vehicle systems and vehicle operational processes affected by the expert vehicle control human interactions. In embodiments, the tracking vehicle operational state information comprises tracking at least one vehicle system element. In embodiments, the at least one vehicle system element is controlled via the vehicle control-facilitating interface. In embodiments, the at least one vehicle system element is affected by the expert vehicle control human interactions.

0377In embodiments, the tracking vehicle operational state information comprises tracking the vehicle operational state information before, during, and after the expert vehicle control human interaction. In embodiments, the tracking vehicle operational state information comprises tracking at least one of a plurality of vehicle control system outputs that result from the expert vehicle control human interactions and vehicle operational results achieved responsive to the expert vehicle control human interactions. In embodiments, the vehicle is to be controlled to achieve results that are consistent with results achieved via the expert vehicle control human interactions.

0378In embodiments, the method further comprises tracking and recording conditions proximal to the vehicle with a plurality of vehicle mounted sensors. In embodiments, the training of the artificial intelligence system is further responsive to the conditions proximal to the vehicle tracked contemporaneously to the expert vehicle control human interactions. In embodiments, the training is further responsive to a plurality of data feeds from remote sensors, the plurality of data feeds comprising data collected by the remote sensors contemporaneous to the expert vehicle control human interactions.

0379<figref idref="DRAWINGS">FIG. <b>55</b></figref> illustrates a method <b>5500</b> for mimicking human operation of a vehicle by robotic process automation of in accordance with embodiments of the systems and methods disclosed herein. At <b>5502</b> the method includes capturing human operator interactions with a vehicle control system interface operatively connected to a vehicle. At <b>5504</b> the method includes capturing vehicle response and operating conditions associated at least contemporaneously with the human operator interaction. At <b>5506</b> the method includes capturing environmental information associated at least contemporaneously with the human operator interaction. At <b>5508</b> the method includes training an artificial intelligence system to control the vehicle with an optimized margin of safety while mimicking the human operator, the artificial intelligence system taking input from the environment data collection module about the instances of environmental information associated with the contemporaneously collected vehicle response and operating conditions. In embodiments, the optimized margin of safety is achieved by training the artificial intelligence system to control the vehicle based on a set of human operator interaction data collected from interactions of an expert human vehicle operator and a set of outcome data from a set of vehicle safety events.

0380Referring to <figref idref="DRAWINGS">FIGS. <b>53</b> and <b>55</b></figref> in embodiments, the method further comprises: applying deep learning of the artificial intelligence system <b>53114</b> to optimize a margin of vehicle operating safety <b>53204</b> by affecting a controlling of at least one aspect of the vehicle through structured variation in control of the at least one aspect of the vehicle to mimic the expert vehicle control human interactions <b>53201</b> and processing feedback from the controlling of the at least one aspect of the vehicle with machine learning. In embodiments, the artificial intelligence system employs a workflow that involves decision-making and the robotic process automation system <b>53181</b> facilitates automation of the decision-making. In embodiments, the artificial intelligence system employs a workflow that involves remote control of the vehicle and the robotic process automation system facilitates automation of remotely controlling the vehicle <b>5310</b>.

0381Referring now to <figref idref="DRAWINGS">FIG. <b>56</b></figref>, a transportation system <b>5611</b> is depicted which includes an interface <b>56133</b> by which a set of expert systems <b>5657</b> may be configured to provide respective outputs <b>56193</b> for managing at least one of a set of vehicle parameters, a set of fleet parameters and a set of user experience parameters.

0382Such an interface <b>56133</b> may include a graphical user interface (such as having a set of visual elements, menu items, forms, and the like that can be manipulated to enable selection and/or configuration of an expert system <b>5657</b>), an application programming interface, an interface to a computing platform (e.g., a cloud-computing platform, such as to configure parameters of one or more services, programs, modules, or the like), and others. For example, an interface <b>56133</b> may be used to select a type of expert system <b>5657</b>, such as a model (e.g., a selected model for representing behavior of a vehicle, a fleet or a user, or a model representing an aspect of an environment relevant to transportation, such as a weather model, a traffic model, a fuel consumption model, an energy distribution model, a pricing model or the like), an artificial intelligence system (such as selecting a type of neural network, deep learning system, or the like, of any type described herein), or a combination or hybrid thereof. For example, a user may, in an interface <b>56133</b>, elect to use the European Center for Medium-Range Weather Forecast (ECMWF) to forecast weather events that may impact a transportation environment, along with a recurrent neural network for forecasting user shopping behavior (such as to indicate likely preferences of a user along a traffic route).

0383Thus, an interface <b>56133</b> may be configured to provide a host, manager, operator, service provider, vendor, or other entity interacting within or with a transportation system <b>5611</b> with the ability to review a range of models, expert systems <b>5657</b>, neural network categories, and the like. The interface <b>56133</b> may optionally be provided with one or more indicators of suitability for a given purpose, such as one or more ratings, statistical measures of validity, or the like. The interface <b>56133</b> may also be configured to select a set (e.g., a model, expert system, neural network, etc.) that is well adapted for purposes of a given transportation system, environment, and purpose. In embodiments, such an interface <b>56133</b> may allow a user <b>5690</b> to configure one or more parameters of an expert system <b>5657</b>, such as one or more input data sources to which a model is to be applied and/or one or more inputs to a neural network, one or more output types, targets, durations, or purposes, one or more weights within a model or an artificial intelligence system, one or more sets of nodes and/or interconnections within a model, graph structure, neural network, or the like, one or more time periods of input, output, or operation, one or more frequencies of operation, calculation, or the like, one or more rules (such as rules applying to any of the parameters configured as described herein or operating upon any of the inputs or outputs noted herein), one or more infrastructure parameters (such as storage parameters, network utilization parameters, processing parameters, processing platform parameters, or the like). As one example among many other possible example, a user <b>5690</b> may configure a selected neural network to take inputs from a weather model, a traffic model, and a real-time traffic reporting system in order to provide a real-time output <b>56193</b> to a routing system for a vehicle <b>5610</b>, where the neural network is configured to have ten million nodes and to undertake processing on a selected cloud platform.

0384In embodiments, the interface <b>56133</b> may include elements for selection and/or configuration of a purpose, an objective or a desired outcome of a system and/or sub-system, such as one that provides input, feedback, or supervision to a model, to a machine learning system, or the like. For example, a user <b>5690</b> may be allowed, in an interface <b>56133</b>, to select among modes (e.g., comfort mode, sports mode, high-efficiency mode, work mode, entertainment mode, sleep mode, relaxation mode, long-distance trip mode, or the like) that correspond to desired outcomes, which may include emotional outcomes, financial outcomes, performance outcomes, trip duration outcomes, energy utilization outcomes, environmental impact outcomes, traffic avoidance outcomes, or the like. Outcomes may be declared with varying levels of specificity. Outcomes may be defined by or for a given user <b>5690</b> (such as based on a user profile or behavior) or for a group of users (such as by one or more functions that harmonizes outcomes according to multiple user profiles, such as by selecting a desired configuration that is consistent with an acceptable state for each of a set of riders). As an example, a rider may indicate a preferred outcome of active entertainment, while another rider may indicate a preferred outcome of maximum safety. In such a case, the interface <b>56133</b> may provide a reward parameter to a model or expert system <b>5657</b> for actions that reduce risk and for actions that increase entertainment, resulting in outcomes that are consistent with objectives of both riders. Rewards may be weighted, such as to optimize a set of outcomes. Competition among potentially conflicting outcomes may be resolved by a model, by rule (e.g., a vehicle owner's objectives may be weighted higher than other riders, a parent's over a child, or the like), or by machine learning, such as by using genetic programming techniques (such as by varying combinations of weights and/or outcomes randomly or systematically and determining overall satisfaction of a rider or set of riders).

0385An aspect provided herein includes a system for transportation <b>5611</b>, comprising: an interface <b>56133</b> to configure a set of expert systems <b>5657</b> to provide respective outputs <b>56193</b> for managing a set of parameters selected from the group consisting of a set of vehicle parameters, a set of fleet parameters, a set of user experience parameters, and combinations thereof.

0386An aspect provided herein includes a system for configuration management of components of a transportation system <b>5611</b> comprising: an interface <b>56133</b> comprising: a first portion <b>56194</b> of the interface <b>56133</b> for configuring a first expert computing system of the expert computing systems <b>5657</b> for managing a set of vehicle parameters; a second portion <b>56195</b> of the interface <b>56133</b> for configuring a second expert computing system of the expert computing systems <b>5657</b> for managing a set of vehicle fleet parameters; and a third portion <b>56196</b> of the interface <b>56133</b> for configuring a third expert computing system for managing a set of user experience parameters. In embodiments, the interface <b>56133</b> is a graphical user interface through which a set of visual elements <b>56197</b> presented in the graphical user interface, when manipulated in the interface <b>56133</b> causes at least one of selection and configuration of one or more of the first, second, and third expert systems <b>5657</b>. In embodiments, the interface <b>56133</b> is an application programming interface. In embodiments, the interface <b>56133</b> is an interface to a cloud-based computing platform through which one or more transportation-centric services, programs and modules are configured.

0387An aspect provided herein includes a transportation system <b>5611</b> comprising: an interface <b>56133</b> for configuring a set of expert systems <b>5657</b> to provide outputs <b>56193</b> based on which the transportation system <b>5611</b> manages transportation-related parameters. In embodiments, the parameters facilitate operation of at least one of a set of vehicles, a fleet of vehicles, and a transportation system user experience; and a plurality of visual elements <b>56197</b> representing a set of attributes and parameters of the set of expert systems <b>5657</b> that are configurable by the interface <b>56133</b> and a plurality of the transportation systems <b>5611</b>. In embodiments, the interface <b>56133</b> is configured to facilitate manipulating the visual elements <b>56197</b> thereby causing configuration of the set of expert systems <b>5657</b>. In embodiments, the plurality of the transportation systems comprise a set of vehicles <b>5610</b>.

0388In embodiments, the plurality of the transportation systems comprise a set of infrastructure elements <b>56198</b> supporting a set of vehicles <b>5610</b>. In embodiments, the set of infrastructure elements <b>56198</b> comprises vehicle fueling elements. In embodiments, the set of infrastructure elements <b>56198</b> comprises vehicle charging elements. In embodiments, the set of infrastructure elements <b>56198</b> comprises traffic control lights. In embodiments, the set of infrastructure elements <b>56198</b> comprises a toll booth. In embodiments, the set of infrastructure elements <b>56198</b> comprises a rail system. In embodiments, the set of infrastructure elements <b>56198</b> comprises automated parking facilities. In embodiments, the set of infrastructure elements <b>56198</b> comprises vehicle monitoring sensors.

0389In embodiments, the visual elements <b>56197</b> display a plurality of models that can be selected for use in the set of expert systems <b>5657</b>. In embodiments, the visual elements <b>56197</b> display a plurality of neural network categories that can be selected for use in the set of expert systems <b>5657</b>. In embodiments, at least one of the plurality of neural network categories includes a convolutional neural network. In embodiments, the visual elements <b>56197</b> include one or more indicators of suitability of items represented by the plurality of visual elements <b>56197</b> for a given purpose. In embodiments, configuring a plurality of expert systems <b>5657</b> comprises facilitating selection sources of inputs for use by at least a portion of the plurality of expert systems <b>5657</b>. In embodiments, the interface <b>56133</b> facilitates selection, for at least a portion of the plurality of expert systems <b>5657</b>, one or more output types, targets, durations, and purposes.

0390In embodiments, the interface <b>56133</b> facilitates selection, for at least a portion of the plurality of expert systems <b>5657</b>, of one or more weights within a model or an artificial intelligence system. In embodiments, the interface <b>56133</b> facilitates selection, for at least a portion of the plurality of expert systems <b>5657</b>, of one or more sets of nodes or interconnections within a model. In embodiments, the interface <b>56133</b> facilitates selection, for at least a portion of the plurality of expert systems <b>5657</b>, of a graph structure. In embodiments, the interface <b>56133</b> facilitates selection, for at least a portion of the plurality of expert systems <b>5657</b>, of a neural network. In embodiments, the interface facilitates selection, for at least a portion of the plurality of expert systems, of one or more time periods of input, output, or operation.

0391In embodiments, the interface <b>56133</b> facilitates selection, for at least a portion of the plurality of expert systems <b>5657</b>, of one or more frequencies of operation. In embodiments, the interface <b>56133</b> facilitates selection, for at least a portion of the plurality of expert systems <b>5657</b>, of frequencies of calculation. In embodiments, the interface <b>56133</b> facilitates selection, for at least a portion of the plurality of expert systems <b>5657</b>, of one or more rules for applying to the plurality of parameters. In embodiments, the interface <b>56133</b> facilitates selection, for at least a portion of the plurality of expert systems <b>5657</b>, of one or more rules for operating upon any of the inputs or upon the provided outputs. In embodiments, the plurality of parameters comprise one or more infrastructure parameters selected from the group consisting of storage parameters, network utilization parameters, processing parameters, and processing platform parameters.

0392In embodiments, the interface <b>56133</b> facilitates selecting a class of an artificial intelligence computing system, a source of inputs to the selected artificial intelligence computing system, a computing capacity of the selected artificial intelligence computing system, a processor for executing the artificial intelligence computing system, and an outcome objective of executing the artificial intelligence computing system. In embodiments, the interface <b>56133</b> facilitates selecting one or more operational modes of at least one of the vehicles <b>5610</b> in the transportation system <b>5611</b>. In embodiments, the interface <b>56133</b> facilitates selecting a degree of specificity for outputs <b>56193</b> produced by at least one of the plurality of expert systems <b>5657</b>.

0393Referring now to <figref idref="DRAWINGS">FIG. <b>57</b></figref>, an example of a transportation system <b>5711</b> is depicted having an expert system <b>5757</b> for configuring a recommendation for a configuration of a vehicle <b>5710</b>. In embodiments, the recommendation includes at least one parameter of configuration for the expert system <b>5757</b> that controls a parameter of at least one of a vehicle parameter <b>57159</b> and a user experience parameter <b>57205</b>. Such a recommendation system may recommend a configuration for a user based on a wide range of information, including data sets indicating degrees of satisfaction of other users, such as user profiles, user behavior tracking (within a vehicle and outside), content recommendation systems (such as collaborative filtering systems used to recommend music, movies, video and other content), content search systems (e.g., such as used to provide relevant search results to queries), e-commerce tracking systems (such as to indicate user preferences, interests, and intents), and many others. The recommendation system <b>57199</b> may use the foregoing to profile a rider and, based on indicators of satisfaction by other riders, determine a configuration of a vehicle <b>5710</b>, or an experience within the vehicle <b>5710</b>, for the rider.

0394The configuration may use similarity (such as by similarity matrix approaches, attribute-based clustering approaches (e.g., k-means clustering) or other techniques to group a rider with other similar riders. Configuration may use collaborative filtering, such as by querying a rider about particular content, experiences, and the like and taking input as to whether they are favorable or unfavorable (optionally with a degree of favorability, such as a rating system (e.g., <b>5</b> stars for a great item of content). The recommendation system <b>57199</b> may use genetic programming, such as by configuring (with random and/or systematic variation) combinations of vehicle parameters and/or user experience parameters and taking inputs from a rider or a set of riders (e.g., a large survey group) to determine a set of favorable configurations. This may occur with machine learning over a large set of outcomes, where outcomes may include various reward functions of the type described herein, including indicators of overall satisfaction and/or indicators of specific objectives. Thus, a machine learning system or other expert systems <b>5757</b> may learn to configure the overall ride for a rider or set of riders and to recommend such a configuration for a rider. Recommendations may be based on context, such as whether a rider is alone or in a group, the time of day (or week, month or year), the type of trip, the objective of the trip, the type or road, the duration of a trip, the route, and the like.

0395An aspect provided herein includes a system for transportation <b>5711</b>, comprising: an expert system <b>5757</b> to configure a recommendation for a vehicle configuration. In embodiments, the recommendation includes at least one parameter of configuration for the expert system <b>5757</b> that controls a parameter selected from the group consisting of a vehicle parameter <b>57159</b>, a user experience parameter <b>57205</b>, and combinations thereof.

0396An aspect provided herein includes a recommendation system <b>57199</b> for recommending a configuration of a vehicle <b>5710</b>, the recommendation system <b>57199</b> comprising an expert system <b>5757</b> that produces a recommendation of a parameter for configuring a vehicle control system <b>57134</b> that controls at least one of a vehicle parameter <b>57159</b> and a vehicle rider experience parameter <b>57205</b>.

0397In embodiments, the vehicle <b>5710</b> comprises a system for automating at least one control parameter of the vehicle <b>5710</b>. In embodiments, the vehicle is at least a semi-autonomous vehicle. In embodiments, the vehicle is automatically routed. In embodiments, the vehicle is a self-driving vehicle.

0398In embodiments, the expert system <b>5757</b> is a neural network system. In embodiments, the expert system <b>5757</b> is a deep learning system. In embodiments, the expert system <b>5757</b> is a machine learning system. In embodiments, the expert system <b>5757</b> is a model-based system. In embodiments, the expert system <b>5757</b> is a rule-based system. In embodiments, the expert system <b>5757</b> is a random walk-based system. In embodiments, the expert system <b>5757</b> is a genetic algorithm system. In embodiments, the expert system <b>5757</b> is a convolutional neural network system. In embodiments, the expert system <b>5757</b> is a self-organizing system. In embodiments, the expert system <b>5757</b> is a pattern recognition system. In embodiments, the expert system <b>5757</b> is a hybrid artificial intelligence-based system. In embodiments, the expert system <b>5757</b> is an acrylic graph-based system.

0399In embodiments, the expert system <b>5757</b> produces a recommendation based on degrees of satisfaction of a plurality of riders of vehicles <b>5710</b> in the transportation system <b>5711</b>. In embodiments, the expert system <b>5757</b> produces a recommendation based on a rider entertainment degree of satisfaction. In embodiments, the expert system <b>5757</b> produces a recommendation based on a rider safety degree of satisfaction. In embodiments, the expert system <b>5757</b> produces a recommendation based on a rider comfort degree of satisfaction. In embodiments, the expert system <b>5757</b> produces a recommendation based on a rider in-vehicle search degree of satisfaction.

0400In embodiments, the at least one rider (or user) experience parameter <b>57205</b> is a parameter of traffic congestion. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of desired arrival times. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of preferred routes. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of fuel efficiency. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of pollution reduction. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of accident avoidance. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of avoiding bad weather. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of avoiding bad road conditions. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of reduced fuel consumption. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of reduced carbon footprint. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of reduced noise in a region. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of avoiding high-crime regions.

0401In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of collective satisfaction. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of maximum speed limit. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of avoidance of toll roads. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of avoidance of city roads. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of avoidance of undivided highways. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of avoidance of left turns. In embodiments, the at least one rider experience parameter <b>57205</b> is a parameter of avoidance of driver-operated vehicles.

0402In embodiments, the at least one vehicle parameter <b>57159</b> is a parameter of fuel consumption. In embodiments, the at least one vehicle parameter <b>57159</b> is a parameter of carbon footprint. In embodiments, the at least one vehicle parameter <b>57159</b> is a parameter of vehicle speed. In embodiments, the at least one vehicle parameter <b>57159</b> is a parameter of vehicle acceleration. In embodiments, the at least one vehicle parameter <b>57159</b> is a parameter of travel time.

0403In embodiments, the expert system <b>5757</b> produces a recommendation based on at least one of user behavior of the rider (e.g., user <b>5790</b>) and rider interactions with content access interfaces <b>57206</b> of the vehicle <b>5710</b>. In embodiments, the expert system <b>5757</b> produces a recommendation based on similarity of a profile of the rider (e.g., user <b>5790</b>) to profiles of other riders. In embodiments, the expert system <b>5757</b> produces a recommendation based on a result of collaborative filtering determined through querying the rider (e.g., user <b>5790</b>) and taking input that facilitates classifying rider responses thereto on a scale of response classes ranging from favorable to unfavorable. In embodiments, the expert system <b>5757</b> produces a recommendation based on content relevant to the rider (e.g., user <b>5790</b>) including at least one selected from the group consisting of classification of trip, time of day, classification of road, trip duration, configured route, and number of riders.

0404Referring now to <figref idref="DRAWINGS">FIG. <b>58</b></figref>, an example transportation system <b>5811</b> is depicted having a search system <b>58207</b> that is configured to provide network search results for in-vehicle searchers.

0405Self-driving vehicles offer their riders greatly increased opportunity to engage with in-vehicle interfaces, such as touch screens, virtual assistants, entertainment system interfaces, communication interfaces, navigation interfaces, and the like. While systems exist to display the interface of a rider's mobile device on an in-vehicle interface, the content displayed on a mobile device screen is not necessarily tuned to the unique situation of a rider in a vehicle. In fact, riders in vehicles may be collectively quite different in their immediate needs from other individuals who engage with the interfaces, as the presence in the vehicle itself tends to indicate a number of things that are different from a user sitting at home, sitting at a desk, or walking around. One activity that engages almost all device users is searching, which is undertaken on many types of devices (desktops, mobile devices, wearable devices, and others). Searches typically include keyword entry, which may include natural language text entry or spoken queries. Queries are processed to provide search results, in one or more lists or menu elements, often involving delineation between sponsored results and non-sponsored results. Ranking algorithms typically factor in a wide range of inputs, in particular the extent of utility (such as indicated by engagement, clicking, attention, navigation, purchasing, viewing, listening, or the like) of a given search result to other users, such that more useful items are promoted higher in lists.

0406However, the usefulness of a search result may be very different for a rider in a self-driving vehicle than for more general searchers. For example, a rider who is being driven on a defined route (as the route is a necessary input to the self-driving vehicle) may be far more likely to value search results that are relevant to locations that are ahead of the rider on the route than the same individual would be sitting at the individual's desk at work or on a computer at home. Accordingly, conventional search engines may fail to deliver the most relevant results, deliver results that crowd out more relevant results, and the like, when considering the situation of a rider in a self-driving vehicle.

0407In embodiments of the system <b>5811</b> of <figref idref="DRAWINGS">FIG. <b>58</b></figref>, a search result ranking system (search system <b>58207</b>) may be configured to provide in-vehicle-relevant search results. In embodiments, such a configuration may be accomplished by segmenting a search result ranking algorithm to include ranking parameters that are observed in connection only with a set of in-vehicle searches, so that in-vehicle results are ranked based on outcomes with respect to in-vehicle searches by other users. In embodiments, such a configuration may be accomplished by adjusting the weighting parameters applied to one or more weights in a conventional search algorithm when an in-vehicle search is detected (such as by detecting an indicator of an in-vehicle system, such as by communication protocol type, IP address, presence of cookies stored on a vehicle, detection of mobility, or the like). For example, local search results may be weighted more heavily in a ranking algorithm.

0408In embodiments, routing information from a vehicle <b>5810</b> may be used as an input to a ranking algorithm, such as allowing favorable weighting of results that are relevant to local points of interest ahead on a route.

0409In embodiments, content types may be weighted more heavily in search results based on detection of an in-vehicle query, such as weather information, traffic information, event information and the like. In embodiments, outcomes tracked may be adjusted for in-vehicle search rankings, such as by including route changes as a factor in rankings (e.g., where a search result appears to be associated in time with a route change to a location that was the subject of a search result), by including rider feedback on search results (such as satisfaction indicators for a ride), by detecting in-vehicle behaviors that appear to derive from search results (such as playing music that appeared in a search result), and the like.

0410In embodiments, a set of in-vehicle-relevant search results may be provided in a separate portion of a search result interface (e.g., a rider interface <b>58208</b>), such as in a portion of a window that allows a rider <b>57120</b> to see conventional search engine results, sponsored search results and in-vehicle relevant search results. In embodiments, both general search results and sponsored search results may be configured using any of the techniques described herein or other techniques that would be understood by skilled in the art to provide in-vehicle-relevant search results.

0411In embodiments where in-vehicle-relevant search results and conventional search results are presented in the same interface (e.g., the rider interface <b>58208</b>), selection and engagement with in-vehicle-relevant search results can be used as a success metric to train or reinforce one or more search algorithms <b>58211</b>. In embodiments, in-vehicle search algorithms <b>58211</b> may be trained using machine learning, optionally seeded by one or more conventional search models, which may optionally be provided with adjusted initial parameters based on one or more models of user behavior that may contemplate differences between in-vehicle behavior and other behavior. Machine learning may include use of neural networks, deep learning systems, model-based systems, and others. Feedback to machine learning may include conventional engagement metrics used for search, as well as metrics of rider satisfaction, emotional state, yield metrics (e.g., for sponsored search results, banner ads, and the like), and the like.

0412An aspect provided herein includes a system for transportation <b>5811</b>, comprising: a search system <b>58207</b> to provide network search results for in-vehicle searchers.

0413An aspect provided herein includes an in-vehicle network search system <b>58207</b> of a vehicle <b>5810</b>, the search system comprising: a rider interface <b>58208</b> through which the rider <b>58120</b> of the vehicle <b>5810</b> is enabled to engage with the search system <b>58207</b>; a search result generating circuit <b>58209</b> that favors search results based on a set of in-vehicle search criteria that are derived from a plurality of in-vehicle searches previously conducted; and a search result display ranking circuit <b>58210</b> that orders the favored search results based on a relevance of a location component of the search results with a configured route of the vehicle <b>5810</b>.

0414In embodiments, the vehicle <b>5810</b> comprises a system for automating at least one control parameter of the vehicle <b>5810</b>. In embodiments, the vehicle <b>5810</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>5810</b> is automatically routed. In embodiments, the vehicle <b>5810</b> is a self-driving vehicle.

0415In embodiments, the rider interface <b>58208</b> comprises at least one of a touch screen, a virtual assistant, an entertainment system interface, a communication interface and a navigation interface.

0416In embodiments, the favored search results are ordered by the search result display ranking circuit <b>58210</b> so that results that are proximal to the configured route appear before other results. In embodiments, the in-vehicle search criteria are based on ranking parameters of a set of in-vehicle searches. In embodiments, the ranking parameters are observed in connection only with the set of in-vehicle searches. In embodiments, the search system <b>58207</b> adapts the search result generating circuit <b>58209</b> to favor search results that correlate to in-vehicle behaviors. In embodiments, the search results that correlate to in-vehicle behaviors are determined through comparison of rider behavior before and after conducting a search. In embodiments, the search system further comprises a machine learning circuit <b>58212</b> that facilitates training the search result generating circuit <b>58209</b> from a set of search results for a plurality of searchers and a set of search result generating parameters based on an in-vehicle rider behavior model.

0417An aspect provided herein includes an in-vehicle network search system <b>58207</b> of a vehicle <b>5810</b>, the search system <b>58207</b> comprising: a rider interface <b>58208</b> through which the rider <b>58120</b> of the vehicle <b>5810</b> is enabled to engage with the search system <b>5810</b>; a search result generating circuit <b>58209</b> that varies search results based on detection of whether the vehicle <b>5810</b> is in self-driving or autonomous mode or being driven by an active driver; and a search result display ranking circuit <b>58210</b> that orders the search results based on a relevance of a location component of the search results with a configured route of the vehicle <b>5810</b>. In embodiments, the search results vary based on whether the user (e.g., the rider <b>58120</b>) is a driver of the vehicle <b>5810</b> or a passenger in the vehicle <b>5810</b>.

0418In embodiments, the vehicle <b>5810</b> comprises a system for automating at least one control parameter of the vehicle <b>5810</b>. In embodiments, the vehicle <b>5810</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>5810</b> is automatically routed. In embodiments, the vehicle <b>5810</b> is a self-driving vehicle.

0419In embodiments, the rider interface <b>58208</b> comprises at least one of a touch screen, a virtual assistant, an entertainment system interface, a communication interface and a navigation interface.

0420In embodiments, the search results are ordered by the search result display ranking circuit <b>58210</b> so that results that are proximal to the configured route appear before other results.

0421In embodiments, search criteria used by the search result generating circuit <b>58209</b> are based on ranking parameters of a set of in-vehicle searches. In embodiments, the ranking parameters are observed in connection only with the set of in-vehicle searches. In embodiments, the search system <b>58207</b> adapts the search result generating circuit <b>58209</b> to favor search results that correlate to in-vehicle behaviors. In embodiments, the search results that correlate to in-vehicle behaviors are determined through comparison of rider behavior before and after conducting a search. In embodiments, the search system <b>58207</b> further comprises a machine learning circuit <b>58212</b> that facilitates training the search result generating circuit <b>58209</b> from a set of search results for a plurality of searchers and a set of search result generating parameters based on an in-vehicle rider behavior model.

0422An aspect provided herein includes an in-vehicle network search system <b>58207</b> of a vehicle <b>5810</b>, the search system <b>58207</b> comprising: a rider interface <b>58208</b> through which the rider <b>58120</b> of the vehicle <b>5810</b> is enabled to engage with the search system <b>58207</b>; a search result generating circuit <b>58209</b> that varies search results based on whether the user (e.g., the rider <b>58120</b>) is a driver of the vehicle or a passenger in the vehicle; and a search result display ranking circuit <b>58210</b> that orders the search results based on a relevance of a location component of the search results with a configured route of the vehicle <b>5810</b>.

0423In embodiments, the vehicle <b>5810</b> comprises a system for automating at least one control parameter of the vehicle <b>5810</b>. In embodiments, the vehicle <b>5810</b> is at least a semi-autonomous vehicle. In embodiments, the vehicle <b>5810</b> is automatically routed. In embodiments, the vehicle <b>5810</b> is a self-driving vehicle.

0424In embodiments, the rider interface <b>58208</b> comprises at least one of a touch screen, a virtual assistant, an entertainment system interface, a communication interface and a navigation interface.

0425In embodiments, the search results are ordered by the search result display ranking circuit <b>58210</b> so that results that are proximal to the configured route appear before other results. In embodiments, search criteria used by the search result generating circuit <b>58209</b> are based on ranking parameters of a set of in-vehicle searches. In embodiments, the ranking parameters are observed in connection only with the set of in-vehicle searches.

0426In embodiments, the search system <b>58204</b> adapts the search result generating circuit <b>58209</b> to favor search results that correlate to in-vehicle behaviors. In embodiments, the search results that correlate to in-vehicle behaviors are determined through comparison of rider behavior before and after conducting a search. In embodiments, the search system <b>58207</b>, further comprises a machine learning circuit <b>58212</b> that facilitates training the search result generating circuit <b>58209</b> from a set of search results for a plurality of searchers and a set of search result generating parameters based on an in-vehicle rider behavior model.

0427Having thus described several aspects and embodiments of the technology of this application, it is to be appreciated that various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be within the spirit and scope of the technology described in the application. For example, those skilled in the art will readily envision a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the embodiments described herein.

0428Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described. In addition, any combination of two or more features, systems, articles, materials, kits, and/or methods described herein, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

0429The above-described embodiments may be implemented in any of numerous ways. One or more aspects and embodiments of the present application involving the performance of processes or methods may utilize program instructions executable by a device (e.g., a computer, a processor, or other devices) to perform, or control performance of, the processes or methods.

0430As used herein, the term system may define any combination of one or more computing devices, processors, modules, software, firmware, or circuits that operate either independently or in a distributed manner to perform one or more functions. A system may include one or more subsystems.

0431In this respect, various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement one or more of the various embodiments described above.

0432The computer readable medium or media may be transportable, such that the program or programs stored thereon may be loaded onto one or more different computers or other processors to implement various ones of the aspects described above. In some embodiments, computer readable media may be non-transitory media.

0433The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that may be employed to program a computer or other processor to implement various aspects as described above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present application need not reside on a single computer or processor, but may be distributed in a modular fashion among a number of different computers or processors to implement various aspects of the present application.

0434Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that performs particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

0435Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

0436Also, as described, some aspects may be embodied as one or more methods. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

0437The present disclosure should therefore not be considered limited to the particular embodiments described above. Various modifications, equivalent processes, as well as numerous structures to which the present disclosure may be applicable, will be readily apparent to those skilled in the art to which the present disclosure is directed upon review of the present disclosure.

0438Detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.

0439The terms “a” or “an,” as used herein, are defined as one or more than one. The term “another,” as used herein, is defined as at least a second or more. The terms “including” and/or “having,” as used herein, are defined as comprising (i.e., open transition).

0440While only a few embodiments of the present disclosure have been shown and described, it will be obvious to those skilled in the art that many changes and modifications may be made thereunto without departing from the spirit and scope of the present disclosure as described in the following claims. All patent applications and patents, both foreign and domestic, and all other publications referenced herein are incorporated herein in their entireties to the full extent permitted by law.

0441The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor. The present disclosure may be implemented as a method on the machine, as a system or apparatus as part of or in relation to the machine, or as a computer program product embodied in a computer readable medium executing on one or more of the machines. In embodiments, the processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like. The processor may be or may include a signal processor, digital processor, embedded processor, microprocessor or any variant such as a co-processor (math co-processor, graphic co-processor, communication co-processor and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, program codes, program instructions and the like described herein may be implemented in one or more thread. The thread may spawn other threads that may have assigned priorities associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor, or any machine utilizing one, may include non-transitory memory that stores methods, codes, instructions and programs as described herein and elsewhere. The processor may access a non-transitory storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.

0442A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In embodiments, the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (called a die).

0443The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software on a server, client, firewall, gateway, hub, router, or other such computer and/or networking hardware. The software program may be associated with a server that may include a file server, print server, domain server, internet server, intranet server, cloud server, and other variants such as secondary server, host server, distributed server and the like. The server may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.

0444The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, social networks, and the like. Additionally, this coupling and/or connection may facilitate remote execution of program across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more location without deviating from the scope of the disclosure. In addition, any of the devices attached to the server through an interface may include at least one storage medium capable of storing methods, programs, code and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

0445The software program may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client, distributed client and the like. The client may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the client. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.

0446The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers and the like. Additionally, this coupling and/or connection may facilitate remote execution of program across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more location without deviating from the scope of the disclosure. In addition, any of the devices attached to the client through an interface may include at least one storage medium capable of storing methods, programs, applications, code and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

0447The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices and other active and passive devices, modules and/or components as known in the art. The computing and/or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM and the like. The processes, methods, program codes, instructions described herein and elsewhere may be executed by one or more of the network infrastructural elements. The methods and systems described herein may be adapted for use with any kind of private, community, or hybrid cloud computing network or cloud computing environment, including those which involve features of software as a service (SaaS), platform as a service (PaaS), and/or infrastructure as a service (IaaS).

0448The methods, program codes, and instructions described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players and the like. These devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute program codes, methods, and instructions stored thereon. Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices. The mobile devices may communicate with base stations interfaced with servers and configured to execute program codes. The mobile devices may communicate on a peer-to-peer network, mesh network, or other communications network. The program code may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store program codes and instructions executed by the computing devices associated with the base station.

0449The computer software, program codes, and/or instructions may be stored and/or accessed on machine readable media that may include: computer components, devices, and recording media that retain digital data used for computing for some interval of time; semiconductor storage known as random access memory (RAM); mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory; optical storage such as CD, DVD; removable media such as flash memory (e.g., USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, off-line, and the like; other computer memory such as dynamic memory, static memory, read/write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.

0450The methods and systems described herein may transform physical and/or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and/or intangible items from one state to another.

0451The elements described and depicted herein, including in flowcharts and block diagrams throughout the figures, imply logical boundaries between the elements. However, according to software or hardware engineering practices, the depicted elements and the functions thereof may be implemented on machines through computer executable media having a processor capable of executing program instructions stored thereon as a monolithic software structure, as standalone software modules, or as modules that employ external routines, code, services, and so forth, or any combination of these, and all such implementations may be within the scope of the present disclosure. Examples of such machines may include, but may not be limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipment, servers, routers and the like. Furthermore, the elements depicted in the flowchart and block diagrams or any other logical component may be implemented on a machine capable of executing program instructions. Thus, while the foregoing drawings and descriptions set forth functional aspects of the disclosed systems, no particular arrangement of software for implementing these functional aspects should be inferred from these descriptions unless explicitly stated or otherwise clear from the context. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein. All such variations and modifications are intended to fall within the scope of this disclosure. As such, the depiction and/or description of an order for various steps should not be understood to require a particular order of execution for those steps, unless required by a particular application, or explicitly stated or otherwise clear from the context.

0452The methods and/or processes described above, and steps associated therewith, may be realized in hardware, software or any combination of hardware and software suitable for a particular application. The hardware may include a general-purpose computer and/or dedicated computing device or specific computing device or particular aspect or component of a specific computing device. The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and/or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine-readable medium.

0453The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software, or any other machine capable of executing program instructions.

0454Thus, in one aspect, methods described above and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. All such permutations and combinations are intended to fall within the scope of the present disclosure.

0455While the disclosure has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure is not to be limited by the foregoing examples but is to be understood in the broadest sense allowable by law.

0456The use of the terms “a” and “an” and “the” and similar referents in the context of describing the disclosure (especially in the context of the following claims) is to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The term “set” should be understood to include a set of a single member or multiple members. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitations of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the disclosure and does not pose a limitation on the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the disclosure.

0457While the foregoing written description enables one skilled to make and use what is considered presently to be the best mode thereof, those skilled in the art will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein. The disclosure should therefore not be limited by the above described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the disclosure.

0458Any element in a claim that does not explicitly state “means for” performing a specified function, or “step for” performing a specified function, is not to be interpreted as a “means” or “step” clause as specified in 35 U.S.C. § 112(f). In particular, any use of “step of” in the claims is not intended to invoke the provision of 35 U.S.C. § 112(f).

0459Those skilled in the art may appreciate that numerous design configurations may be possible to enjoy the functional benefits of the inventive systems. Thus, given the wide variety of configurations and arrangements of embodiments of the present disclosure, the scope of the disclosure is reflected by the breadth of the claims below rather than narrowed by the embodiments described above.